U.S. patent application number 11/056889 was filed with the patent office on 2005-07-07 for method and system for ascribing a reputation to an entity as a rater of other entities.
This patent application is currently assigned to Open Ratings, Inc.. Invention is credited to Tkach, Dmitry, Zacharia, Giorgos C..
Application Number | 20050149383 11/056889 |
Document ID | / |
Family ID | 34576232 |
Filed Date | 2005-07-07 |
United States Patent
Application |
20050149383 |
Kind Code |
A1 |
Zacharia, Giorgos C. ; et
al. |
July 7, 2005 |
Method and system for ascribing a reputation to an entity as a
rater of other entities
Abstract
A method and system for ascribing a reputation of an entity as a
rater of other entities is provided. A first rating indicative of a
rating of a rated entity by the first entity, and one or more
second ratings, each second rating indicative of a rating of the
rated entity provided by another entity, are provided. The second
ratings are combined to produce a first combined rating. The first
rating is compared to the first combined rating to produce a first
rating predictability of the first rating, the first rating
predictability being a negative function of a magnitude of a
difference between the first rating and the first combined rating.
A resulting rater reputation is generated based at least in part on
the first rating predictability.
Inventors: |
Zacharia, Giorgos C.;
(Lakatamia, CY) ; Tkach, Dmitry; (Peabody,
MA) |
Correspondence
Address: |
Daniel P. McLoughlin
Wolf, Greenfield & Sacks, P.C.
600 Atlantic Avenue
Boston
MA
02210-2206
US
|
Assignee: |
Open Ratings, Inc.
Boston
MA
|
Family ID: |
34576232 |
Appl. No.: |
11/056889 |
Filed: |
February 11, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11056889 |
Feb 11, 2005 |
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09710008 |
Nov 10, 2000 |
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6895385 |
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60209059 |
Jun 2, 2000 |
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Current U.S.
Class: |
705/7.29 |
Current CPC
Class: |
G06Q 10/04 20130101;
G06Q 30/0201 20130101; G06Q 30/02 20130101 |
Class at
Publication: |
705/010 |
International
Class: |
G06F 017/60 |
Claims
What is claimed is:
1. A method of determining a rater reputation of a first entity as
a rater of other entities, wherein provided is a first rating
indicative of a rating of a rated entity by the first entity, and
one or more second ratings, each second rating indicative of a
rating of the rated entity provided by another entity, the method
comprising acts of: (A) combining the second ratings to produce a
first combined rating; (B) comparing the first rating to the first
combined rating to produce a first rating predictability of the
first rating, the first rating predictability being a negative
function of a magnitude of a difference between the first rating
and the first combined rating; and (C) generating a resulting rater
reputation based at least in part on the first rating
predictability.
Description
RELATED APPLICATIONS
[0001] This application claims the benefit under 35 U.S.C.
.sctn.120 of U.S. application Ser. No. 09/710,008, titled "Method
And System For Ascribing A Reputation To An Entity As A Rater Of
Other Entities," filed on Nov. 10, 2000, which claims the benefit
under 35 U.S.C. .sctn. 119(e) of commonly-owned, U.S. provisional
patent application Ser. No. 60/209,059, titled, "Determining a
Reputation of an Entity", filed Jun. 2, 2000, both of which are
incorporated herein by reference in their entirety.
[0002] Further, each of the following related commonly-owned U.S.
patent applications: "Method and System for Ascribing a Reputation
to an Entity from the Perspective of Another Entity" by Giorgos
Zacharia, "System and Method for Estimating the Impacts of Multiple
Ratings on a Result" by Giorgos Zacharia, "System and Method for
Ascribing a Reputation to an Entity" by Giorgos Zacharia, and
"System and Method for Recursively Estimating a Reputation of an
Entity" by Giorgos Zacharia, each application filed on Nov. 10,
2000, is herein incorporated by reference in its entirety.
BACKGROUND OF THE INVENTION
[0003] The emergence of the Internet and other large networks has
increased both the number and kinds of electronic exchanges between
entities. As used herein, an electronic exchange is any exchange
between two or more entities over an electronic network (i.e., not
in person) such as, for example, a voice communications network
(e.g., POTS or PBX) or a data communications network (e.g., LAN or
the Internet) or a voice and data communications network (e.g.,
voice-over-IP network). Electronic exchanges may include electronic
business transactions and electronic communications. Such
electronic business transactions may include the negotiation and
closing of a sale of goods or services, including solicitation of
customers, making an offer and accepting an offer. For example, in
consumer-to-consumer electronic marketplaces (e.g., the eBay,
OnSale, Yahoo and Amazon marketplaces found on the global Internet
entities may transact for the sale and purchase of goods or
services.
[0004] Electronic communications also may include communications in
on-line communities such as mailing lists, news groups, or
web-based message boards and chatrooms, where a variety of
sensitive personal information may be exchanged, including
health-related data, financial investment data, help and advise on
research and technology-related issues, or even information about
political issues. As referred to herein, an entity may be a person
or an electronic agent (e.g., a software agent). Such a person may
Act as an individual (i.e., on the person's own behalf) or as a
representative (e.g., officer or agent) of a corporation,
partnership, agency, organization, or other group. An electronic
agent may act as an agent of an individual, corporation,
partnership, agency, organization, or other group.
[0005] In many electronic exchanges, an entity's identity may be
anonymous to another entity. This anonymity raises several issues
regarding trust and deception in connection to these exchanges. For
example, an anonymous entity selling goods on-line may misrepresent
the condition or worth of a good to a buyer without suffering a
loss of reputation, business or other adverse effect, due to the
entity's anonymity.
[0006] One solution to the problems regarding trust and deception
is to provide a reputation mechanism to determine and maintain a
reputation or reliability rating of an entity. Typically, a
reputation mechanism is intended to provide an indication of how
reliable an entity is, i.e., how truly its actions correspond to
its representations, based on feedback by other entities that have
conducted an electronic exchange with the entity. Such feedback
typically is provided by another entity in the form of ratings in a
numerical (e.g., 1-5) or Boolean (e.g., good or bad) form. In some
reputation mechanisms, an average of the ratings provided by other
entities are calculated to produce the reputation rating of the
entity. Consequently, such reputation mechanisms typically
represent the reputation of an entity with a scalar value.
[0007] Typical reputation mechanisms suffer from susceptibility to
frauds or deceptions. For example, a first typical fraud occurs
when an anonymous entity, after developing a poor reputation over
time in an on-line community, reenters the community with a new
anonymous identity (i.e., on-line name), thereby starting anew with
a higher reputation than the entity's already earned poor
reputation. A second typical fraud, to which typical reputation
mechanisms are susceptible, occurs when two or more entities
collude to provide high ratings for each other on a relatively
frequent basis, such that the reputations of these entities are
thereby artificially inflated.
[0008] Two reputation mechanisms that solve these two problems,
Sporas and Histos, are disclosed in "Collaborative Reputation
Mechanisms for On-line Communities" by Giorgos Zacharia, submitted
to the Program of Media Arts and Sciences, Massachusetts Institute
of Technology, Cambridge, Mass. published September, 1999
(hereinafter "the Zacharia thesis"), the contents of which are
herein incorporated by reference.
[0009] Sporas is a reputation mechanism for loosely-connected
communities (i.e., one in which many entities may not have had an
electronic exchange with one another and thus not have rated one
another.) According to the Sporas technique, a reputation may be
calculated for an entity by applying the following equation: 1
Equation1: R i = R i - 1 + 1 C .cndot. damp ( R i - 1 ) R i other (
W i - E i ) ,
[0010] where R.sub.i-1 is the initial reputation of the entity, C
is an effective number of ratings, 1/C is the change rate factor,
named as such because it impacts the rate at which the reputation
changes, damp (R.sub.i-1) is a damping function,
R.sub.i.sup.otheris the reputation of another entity providing the
rating, W.sub.i is the rating of the entity provided by the other
entity, E.sub.i is the expected value of the rating and R.sub.i is
the reputation of the entity.
[0011] Zacharia discloses that the damping function may be
calculated by applying the following equation: 2 Equation2: damp (
R i - 1 ) = 1 - 1 1 + - ( R i - 1 - D ) a ,
[0012] where D is the size of the range of allowed reputation
values and a is a so-called "acceleration" factor. The acceleration
factor is named as such because its value controls a rate at which
an entity's reputation changes. The Zacharia thesis further
discloses that an expected rating, E.sub.i can be calculated from
the following equation: 3 Equation3: E i = R i - 1 D .
[0013] (Throughout this application, if a value represented by a
symbol from a current equation was described in connection with a
previously-described equation, the description of the value will
not be repeated for the current equation.)
[0014] The Sporas technique implements an entity reputation
mechanism based on the following principles. First, new entities
start with a minimum reputation value, and build-up their
reputations as a result of their activities on the system. For
example, if a reputation mechanism has a rating range from 1 to
100, then an entity may start with an initial reputation value,
R.sub.0, of 1. By starting with the minimum reputation value,
Sporas reduces the incentive to, and effectively eliminates, that
ability of an entity with a low reputation to improve the entity's
reputation by reentering the system as a new anonymous
identity.
[0015] Second, the reputation of an entity never falls below the
reputation of a new entity. This may be ensured by applying
equation 1 above. This second principle also reduces the incentive,
and effectively prevents, an entity with a low reputation from
reentering the system as a new anonymous entity.
[0016] Third, after each electronic exchange, the reputations of
each of the two or more entities involved are updated according to
the feedback or ratings provided by the other entities, where the
feedback or ratings represent the demonstrated trustworthiness of
the two or more entities in the latest exchange. For example,
referring to Equation 1 above, the ratee reputation R.sub.i of an
entity is updated for each new rating, W.sub.i.
[0017] Fourth, two entities may rate each other only once. If two
entities exchange more than once, then, for each entity, the
reputation mechanism only applies the most recently submitted
rating to determine the reputation of the rated entity. This fourth
principle prevents two or more entities from fraudulently inflating
their reputations, as describe above, by frequently rating each
other with artificially high ratings.
[0018] Fifth, entities with very high reputation values experience
smaller rating changes after each update. This fifth principle is
implemented by the damping function, damp(R.sub.i-1), of Equations
1 and 2 above. The damping function, increases as the ratee
reputation of the rated entity decreases, and decreases as the
ratee reputation of the rated entity increases. Thus, a high
reputation is less susceptible to change by a single poor rating
provided by another entity.
[0019] Sixth, the reputation mechanism adapts to changes in an
entity's behavior. For example, a reputation may be discounted over
time so that the most recent ratings of an entity have more weight
in determining the ratee reputation of the entity. For example, in
Equation 1, above, ratings are discounted over time by limiting the
effective number of ratings considered, C.
[0020] The Sporas reputation mechanism also weights the reputation
of a rated entity according to the reputation, R.sup.other, of
another entity providing the rating, where this reputation of the
other entity may be determined by applying Equation 1. Therefore,
ratings from entities having relatively higher reputations have
more of an impact on the reputation of the rated entity than
ratings from entities having relatively lower reputations.
[0021] As described in the Zacharia thesis, Histos is a reputation
mechanism better-suited for a highly-connected community, where
entities have provided ratings for a significant number of the
other entities. Histos determines a personalized reputation of a
first entity from a perspective of a particular entity.
[0022] Histos represents the principle that a person or entity is
more likely to trust the opinion of another person or entity with
whom she is familiar than trust the opinion of another person or
entity who she does not know. Unlike Sporas, a reputation of first
entity in Histos depends on the second entity from whose
perspective the determination is made, and other ratings of the
second entity provided by other users in an on-line community or
population.
[0023] FIG. 1 is a block diagram illustrating a representation of
an on-line community or population 300 of entities A.sub.1-A.sub.11
interconnected by several rating links, including rating links 302,
303, 304, 306, 308 and 310. Each rating link indicates a rating of
a rated entity (i.e., a ratee) by a rating entity (i.e., a rater)
with an arrowhead pointing from the rating entity to the rated
entity. As used herein, a ratee is an entity in a position of being
rated by one or more other entities, and a rater is an entity in a
position of rating one or more other entities. For example, rating
link 302 represents a rating of 0.8 for ratee A.sub.3 by rater
A.sub.1, and rating link 303 represents a rating of 0.9 for ratee
A.sub.1by rater A.sub.3.
[0024] Although in FIG. 1, each rating link only indicates a single
rating, it is possible that an entity has provided more than one
rating for another entity. The Zacharia reference discloses that if
entity has provided more than rating for another entity, the most
recent rating should be selected to determine a personalized
reputation of a first entity from the perspective of a second
entity.
[0025] To determine a personalized reputation of a first entity
from the perspective of a second entity, the first and second
entity must be "connected". A first and second entity are connected
if a rating path connects the first and second entity. A rating
path is a series of rating links that connect a first entity to a
second entity. For example, in FIG. 1, entities A.sub.1 and
A.sub.11 are connected by several rating paths, including rating
paths 312 and 314. Rating path 312 includes rating links 302, 304
and 310, and rating path 314 includes rating links 302, 306 and
308.
[0026] As described in the Zacharia thesis, and referring to FIG.
1, to determine a personalized reputation of a first user from the
perspective of a second user, the following methodology may be
applied. First, a breadth-first search algorithm is applied to find
all of the rating paths connecting A.sub.1 to A.sub.11 that are of
a length less than or equal to a specified value. If a rating link
indicates more than one rating, then the most recent rating is
selected for the determination of the personalized reputation.
[0027] The number of rating links included in a rating path is
referred to herein as the "length" of the rating path. For example,
the rating path 312 has a length=3 because it includes three rating
links 302, 304 and 310. Further, an entity included along a rating
path between the first rated entity and the second rating entity
has a "level" equal to a number of links between the entity and the
second entity. For example, in FIG. 1, the entity A.sub.8 is
disposed along the rating path 314. The entity A.sub.8 has a level
2 in the context of the rating path 314 because two rating links
302 and 306 lie between the entity A.sub.8 and the second entity
A.sub.1. Further, an entity having a level, L, may be said to be a
distance L away from the second entity.
[0028] Accordingly, the personalized reputation of a first entity
from the perspective of a second entity may be determined by
application of the following equation: 4 Equation4: R k ( n ) = D
.cndot. [ R j ( n - 1 ) .cndot. W jk ( n ) ] R j ( n - 1 ) ,
[0029] where R.sub.k(n) is the personalized ratee reputation of an
entity k from a perspective of a second entity a distance n from
the entity k, W.sub.jk(n) is a rating provided by an entity j for
the entity k a distance n-1 from the second entity, R.sub.j(n-1) is
the personalized ratee reputation of the entity j from the
perspective of the second entity, and D is a range of allowable
personalized reputation values.
[0030] Referring to FIG. 1, the following example illustrates
applying Equation 4 to determine the personalized ratee reputation
of entity A.sub.11 from the perspective of entity A.sub.1, where
D=1. 5 R 11 = R 9 ( .2 ) + R 8 ( .9 ) R 8 + R 9 , where R 9 = R 3 (
.2 ) R 3 = .2 , and where R 8 = R 3 ( .6 ) R 3 - .6 , such that R
11 = .2 ( .2 ) + .6 ( .9 ) .8 = .725 ,
SUMMARY OF THE INVENTION
[0031] The Sporas reputation mechanism described in the Zacharia
thesis uses a single reputation value to represent the reputation
of an entity both as a ratee and a rater. This single reputation
represents the reputation of an entity as a combination of ratings
provided by other entities, but does not provide an indication of
the reputation or trustworthiness of an entity as a rater of other
entities. Thus, an entity may receive high ratings from other
entities and thus have a high single-valued reputation, although
the entity is a poor rater of other entities and thus should not
truly have a high reputation as a rater of other entities. As used
herein, a "rater reputation" of an entity is a reputation or
trustworthiness of the entity as a rater of one or more other
entities, and a "ratee reputation" of an entity is the reputation
of the entity according to the ratings of the entity provided by
one or more other entities.
[0032] Therefore, Sporas does not provide a method or system for
determining and maintaining a rater reputation of an entity.
[0033] Accordingly, provided herein is a method and system for
determining a rater reputation of an entity. Further, provided is a
method and system for determining a ratee reputation of an entity
based at least in part on the rater reputations of one or more of
the entities that have rated the rated entity.
[0034] In one embodiment, a rater reputation of a first entity as a
rater of other entities is determined. A first rating indicative of
a rating of a rated entity by the first entity, and one or more
second ratings, each second rating indicative of a rating of the
rated entity provided by another entity, are provided. The second
ratings are combined to produce a first combined rating. The first
rating is compared to the first combined rating to produce a first
rating predictability of the first rating, the first rating
predictability being a negative function of a magnitude of a
difference between the first rating and the first combined rating.
A resulting rater reputation is generated based at least in part on
the first rating predictability.
[0035] This embodiment may be implemented as a computer program
product that includes a computer readable medium and computer
readable signals stored on the computer readable medium that define
instructions. These instructions, as a result of being executed by
a computer, instruct the computer to perform the Acts described
above for this embodiment.
[0036] In another embodiment, a system for determining a rater
reputation of a first entity as a rater of other entities is
provided. A first rating indicative of a rating of a rated entity
by the first entity, and one or more second ratings, each second
rating indicative of a rating of the rated entity provided by
another entity, is provided to the system. The system includes a
rater reputation generator to combine the second ratings to produce
a first combined rating. The rater reputation generator is further
operative to compare the first rating to the first combined rating
to produce a first rating predictability of the first rating, the
first rating predictability being a negative function of a
magnitude of a difference between the first rating and the first
combined rating. The rater reputation generator is further
operative to generate a resulting rater reputation based at least
in part on the first rating predictability, and to output this
generated rater reputation.
[0037] In yet another embodiment, a system for determining a rater
reputation of a first entity as a rater of other entities is
provided. A first rating indicative of a rating of a rated entity
by the first entity, and one or more second ratings, each second
rating indicative of a rating of the rated entity provided by
another entity are provided for the system. The system includes:
means for combining the second ratings to produce a first combined
rating; means for comparing the first rating to the first combined
rating to produce a first rating predictability of the first
rating, the first rating predictability being a negative function
of a magnitude of a difference between the first rating and the
first combined rating; and means for generating a resulting rater
reputation based at least in part on the first rating
predictability.
[0038] In another embodiment, a ratee reputation of a first entity
is determined. A first rating of the first entity by a second
entity is received. One or more rater reputations including a first
rater reputation of the second entity as a rater of other entities
are accessed, and a ratee reputation of the first entity is
generated by combining the one or more rater reputations and the
first rating.
[0039] This embodiment may be implemented as a computer program
product that includes a computer readable medium and computer
readable signals stored on the computer readable medium that define
instructions. These instructions, as a result of being executed by
a computer, instruct the computer to perform the Acts described
above for this embodiment.
[0040] In another embodiment, a system for determining a ratee
reputation of a first entity is provided. The system includes a
ratee reputation generator to receive as input a first rating of
the first entity by a second entity. The ratee reputation generator
is operative to access one or more rater reputations including a
first rater reputation of the second entity as a rater of other
entities, and to generate a ratee reputation of the first entity by
combining the one or more rater reputations and the first rating
signal. The ratee reputation generator is further operative to
provide as output the generated ratee reputation.
[0041] In yet another embodiment, a system for determining a ratee
reputation of a first entity is provided. The system includes:
means for receiving a first rating of the first entity by a second
entity; means for accessing one or more rater reputations including
a first rater reputation of the second entity as a rater of other
entities; and means for generating a ratee reputation of the first
entity by combining the one or more rater reputations and the first
rating signal.
[0042] The features and advantages of the invention described above
and other features and advantages of the invention will be more
readily understood and appreciated from the detailed description
below, which should be read together with the accompanying drawing
figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0043] In the drawings:
[0044] FIG. 1 is a block diagram illustrating a representation of
an on-line community of entities interconnected by several rating
links;
[0045] FIG. 2 is a flowchart illustrating an example embodiment of
a method of determining a rater reputation of an entity;
[0046] FIG. 3 is a flow chart illustrating an example embodiment of
a method of combining rating predictabilities to produce a rater
reputation;
[0047] FIG. 4 is data flow diagram illustrating an example
embodiment of a system for generating a rater reputation of an
entity;
[0048] FIG. 5 is a data flow diagram illustrating an example
embodiment of a system for generating a rater reputation of an
entity;
[0049] FIG. 6 is a data flow diagram illustrating an example
embodiment of a system for generating a ratee reputation deviation
of an entity;
[0050] FIG. 7 is a data flow diagram illustrating an example
embodiment of a system for generating a rater reputation deviation
of an entity;
[0051] FIG. 8 is a flowchart illustrating an example embodiment of
a method of generating a ratee reputation of an entity;
[0052] FIG. 9 is a data flow diagram illustrating an example
embodiment of a system for generating a ratee reputation of an
entity;
[0053] FIG. 10 is a flowchart illustrating an example embodiment of
a method of determining a personalized ratee reputation;
[0054] FIG. 11 is a data flow diagram illustrating an example
embodiment of a system for determining a personalized ratee
reputation;
[0055] FIG. 12 is a flow chart illustrating an example embodiment
of recursively estimating the impact of one or more ratings on a
result;
[0056] FIG. 13 is a data flow diagram illustrating an example
embodiment of a system for recursively estimating the impact of one
or more ratings on a result;
[0057] FIG. 14 is a flowchart illustrating an example embodiment of
a method of generating a weighting vector for a set of multiple
ratings;
[0058] FIG. 15 is a data flow diagram illustrating an example
embodiment of a system for generating a weighting vector for a set
of multiple ratings;
[0059] FIG. 16 is a flow chart illustrating an example embodiment
of a method of combining multiple estimated reputations to produce
a new reputation;
[0060] FIG. 17 is a data flow diagram illustrating an example
embodiment of a system for combining multiple estimated reputations
to produce a new estimated reputation; and
[0061] FIG. 18 is a data flow diagram illustrating an example
system architecture for implementing the methods and systems of
FIGS. 2-17.
DETAILED DESCRIPTION
[0062] I. Determining a Rater Reputation
[0063] Described below is a method and system for determining a
rater reputation of a first entity. Although determining a rater
reputation is described below primarily in connection to ratings
developed in connection with electronic exchanges, such
determination may be applied to any of a variety of ratings,
regardless of whether the rating is provided as a result of an
electronic exchange. For example, a rating may represent a
qualitative assessment of an in-person interview of a job
candidate, an in-person sale or a credit transaction.
[0064] The rater reputation produced using the method and system
described below may have any of a variety of applications such as,
for example, assessing the trustworthiness (as a rater) of a
reference, an employment recruiter, a rating agency, or any other
entity that provides recommendations, scores, rankings or ratings
of other entities.
[0065] A rater reputation may be determined, for one or more
ratings of one or more rated entities, respectively, provided by a
first entity, by comparing each rating to other ratings of the
rated entity provided by other entities. For each rated entity, the
statistical similarity between the rating provided by the first
entity and ratings provided by the other entities as determined by
the comparison, i.e., the rating predictability, may serve as a
basis for a rater reputation of the first entity.
[0066] Optionally, the ratings provided by the other entities may
be ratings provided at points in time that occur after a point in
time at which the rating by the first entity is provided.
Determining a rater reputation of a first entity by comparing
ratings provided by the first entity to such future ratings may
provide a more accurate estimate of the first entity's rater
reputation than comparing these ratings to past ratings of other
entities. This more accurate estimate may result from the fact that
if past ratings are used for the comparisons, and the first entity
is aware that past ratings are being used, the first entity may
access the past ratings and bias her ratings to be consistent with
the past ratings, thus resulting in a higher reputation of the
first entity than otherwise would occur.
[0067] Accordingly, by using future ratings, which are necessarily
unknown to the first user, for the comparisons, the first entity
has less incentive to bias her ratings, resulting in more honest
ratings being provided by the first entity. These more honest
ratings result in a more accurate estimation of the rater
reputation of the first entity.
[0068] In an aspect of determining a rater reputation of an entity,
the result of the comparison of (a) the rating provided by the
first rater of a rated entity and (b) other ratings of the rated
entity provided by other raters may be weighed over a ratee
reputation deviation of the rated entity, as will be described in
more detail below in relation to FIG. 5. This ratee reputation
deviation represents a deviation of ratings of the rated entity
from an expected value of the rating of the rated entity. Entities
whose ratee reputations fluctuate over a wide range of values, such
as new entities and entities that receive a wide range of ratings
(i.e., unstable entities), typically have high ratee reputation
deviations. This weighting of the comparison results in a rating
predictability that is greater for greater values of ratee
reputation deviation and less for lesser values of ratee reputation
deviation.
[0069] FIG. 2 is a flowchart illustrating an example embodiment of
a method of generating a rater reputation of an entity, where a
first rating of a rated entity by a first entity and second ratings
of the rated entity provided by other entities are provided. The
first and second ratings may be provided by accessing two or more
entries of a data structure such as, for example, a database,
stored on a computer-readable medium such as, for example, a
non-volatile memory (e.g., a magnetic disk, CD ROM, or magnetic
tape) or a volatile memory (e.g., an integrated semiconductor
memory such as RAM). Such a data structure may reside on a
computer-readable medium located on a same computer at which an
application running the method of generating resides and may be
accessed by a bus or other known means. Alternatively, the data
structure may reside at a remote location from such application,
where access of the data structure may include use of one or more
networks, bridges, routers, switches, hubs, other network devices,
or any combination thereof. Such access may include wireless and
wire (i.e., cable) transmissions.
[0070] In Act 2, two or more of the second ratings of the rated
entity may be combined to produce an expected rating. This expected
rating represents an expected value of the first rating in
consideration of the values of the two or more second ratings. The
two or more ratings to be combined may be selected according to
each rating's temporal proximity to the rating provided by the
first entity (i.e., the first rating), or may be selected with
other criteria such as, for example, demographic similarity of the
entity that provided the second rating to the first entity.
Optionally, only second ratings provided temporally after the first
rating are selected. Such selected second ratings may be referred
to herein as future ratings. Although all of the second ratings may
be selected, which may produce a more accurate expected rating, the
computational time and cost will increase as the number of second
ratings combined increases; consequently, it may be desirable to
select less than all of the second ratings.
[0071] A limiting factor may be provided to determine a number of
the second ratings to select. The value of the limiting factor may
be selected to achieve an acceptable balance between (a) rater
reputation accuracy and (b) the time and cost of determining the
rater reputation.
[0072] In an aspect of selecting second ratings, second ratings
provided by the first entity are excluded from selection. Such
exclusion prevents the first entity from providing multiple ratings
of the rated entity, and intentionally providing a same or similar
value as each of the multiple ratings to artificially increase the
first entity's rater reputation.
[0073] The selected second ratings may be combined by calculating
an average of the second ratings. Optionally, this calculated
average may be weighted, where each selected second rating is
weighted according to the reputation of the entity that provided
the second rating relative to the reputation of the other entities
that provided second ratings. For example, a weighted average of
second ratings provided by other entities may be determined by
applying the following equation: 6 Equation5: E jk ratee , other =
u = j + 1 k W u R u rater u = j + 1 k R u rater ,
[0074] where k-j is the number of selected second ratings,
E.sub.jk.sup.ratee,other is the weighted average of the k-j second
ratings, W.sub.u is the rating provided by a second entity u, and
R.sub.u.sup.rater is the rater reputation of second entity u.
Although applying Equation 5 represents combining only future
rating (from j+1 to k), other second ratings may be combined. For
example, in Equation 5, above, u may begin at u=j-1-C/2 and end at
k=j-1+C/2, where 1/C is a change rate factor, described in more
detail below in relation to Act 12. A situation in which it may be
desirable to determine an expected rating by combining selected
second ratings other than future ratings is described below in more
detail in relation to "seeding" a system.
[0075] Weighting each second rating according to the reputation of
the entity that provided the second rating generates an expected
rating that gives more weight to ratings by those entities that
have higher rater reputations. For example, if most of the higher
second ratings were provided by entities having a low rater
reputation, and most of the lower second ratings were provided by
entities having a higher rater reputation, then the calculated
expected rating would be lower than a raw average. This lower
expected rating represents a bias towards the second ratings
provided by the entities having a higher rater reputation. Such
weighting of each selected second rating represents the principle
that entities having higher rater reputations give more accurate
ratings, and thus the ratings they provide are more valuable in
determining a rater reputation of an entity (by definition) and,
therefore, should be given more weight than ratings provided by
entities having lower rater reputations.
[0076] The average rating of the selected second ratings also may
be weighted according to the relative proximity of the times at
which the selected second ratings are provided to the time at which
the first rating is provided. In other words, a rating provided
closer in time to the first rating time will have a greater weight
and thus more of an impact on the calculated average than a
temporally more distant rating. This weighting reflects the
principle that an entity's ratee reputation may change over time.
Thus, for a given time at which a first rating is compared to other
ratings, temporally closer ratings should be given more weight in
determining the rater reputation of the first entity. Conversely,
temporally more distant ratings should have lesser weight in
determining the rater reputation of the first entity.
[0077] If only second ratings occurring after the first point in
time (i.e., future ratings) are selected, then the earlier the
second rating is provided, the greater is the weight attributed to
the second rating. Further, time may be divided into a number of
intervals, where each second ratings is provided during one of the
intervals. Each second rating may be weighted according to the
temporal proximity to the first point in time of the interval in
which the second rating was provided.
[0078] Optionally, each selected second rating may be placed in a
temporal order according to its relative temporal proximity to the
first point in time, and each selected second rating may be
weighted according to its position in this order. If only future
ratings are selected, then the earlier the second rating is
provided, the lower (i.e., the closer to the beginning) the rating
is in the temporal order. Thus, a temporally-weighted average may
be defined by the following equation: 7 Equation6: E jk ratee ,
other = u = j + 1 k W u f ( u ) u = j + 1 k f ( u ) ,
[0079] where .function.(u) is a temporal function such as, for
example: 8 Equation7: f ( u ) = 1 T u ,
[0080] where T is a constant number having a value>1, and u may
be: the position of rating W.sub.u in a temporal order; a temporal
proximity of the rating from the first point in time; or a temporal
proximity of a time interval in which the rating was provided to
the first point in time.
[0081] Combining Equations 6 and 7, an expected rating may be
calculated by applying the following equation: 9 Equation8: E jk
ratee , others = u = j + 1 k W u R u rater f ( u ) u = j + 1 k R u
rater f ( u ) .
[0082] Other statistical methods may be used to determine an
expected rating of an entity.
[0083] Returning to the method of FIG. 2, next, in Act 4, the first
rating of the rated entity, provided by the first entity, may be
compared to the expected rating, to produce a rating
predictability. This rating predictability may be a negative
function of an absolute difference between the first rating and the
expected rating. As used herein, a first value is a negative
function of a second value if the first value decreases as a result
of the second value increasing, and increases as a result of the
second value decreasing. Further, as used herein, a first value is
a positive function of a second value if the first value increases
as a result of the second value increasing, and decreases as a
result of the second value decreasing.
[0084] For example, comparing the first ratings and the second
ratings may include determining the absolute difference (i.e.,
magnitude of the difference) between the first rating and the
second rating and then applying a Gaussian distribution function to
the determined absolute difference to produce the first rating
predictability. If a Gaussian distribution function is being
applied, then the absolute difference between the first rating and
the expected rating may first be divided by a ratee reputation
deviation of the rated entity. The ratee reputation deviation
defines a deviation of ratings by other entities of the rated
entity from an expected rating, and is described in more detail
below in relation to FIG. 6. Thus, a rating predictability for the
first rating may be determined by applying the following equation:
10 Equation9: P j ( X ) = 1 2 .cndot. - x 2 2 , X = W j - E jk
ratee , others RD j - 1 ratee ,
[0085] where 11 RD j - 1 ratee
[0086] is the ratee reputation deviation of the rated entity, and
P.sub.j(X) is the rating predictability of the first rating
W.sub.j.
[0087] Dividing the absolute difference by the rates reputation
duration has the effect of weighting the absolute difference over
the ratee reputation deviation. This weighting may be desirable to
account for the stability or reliability of rater ratings of the
rated entity in determining the rates reputation of the first
entity. For example, if the ratee reputation of the rated entity is
unstable (i.e., ratings provided for the rated entity vary
considerably), the rater reputation of the first entity should be
changed less by the difference between the expected rating and the
first rating. Conversely, if the ratee reputation of the rated
entity is stable, the rater reputation of the first entity should
be changed more by the difference.
[0088] Alternatively, if the first entity has provided other
ratings of other entities, and rating predictabilities have been
generated from these other ratings (e.g., by performing Acts 2 and
4 on the provided ratings), then, in Act 5, the other rating
predictabilities may be combined with the rating predictability
generated in Act 4 to produce the rater reputation of the first
entity. Act 5 may be implemented in any of several different ways.
In one implementation, Act 5 may be implemented by averaging all of
the generated rating predictabilities associated with each rating
provided by the first entity. Accordingly, a rater reputation may
be determined by applying the following equation: 12 Equation11: R
rater = 1 n .cndot. j = 1 n P j ( X ) ,
[0089] where 13 RD min ratee
[0090] is the minimum allowed ratee reputation deviation. Other
methods may be used to determine a rating predictability of an
entity.
[0091] If the first rating is a first-time rating by the first
entity, then the generated rating predictability may alone serve as
the rater reputation of the first entity.
[0092] Alternatively, if the first entity has provided other rating
of other entities, and rating predictabilities have been generated
from these other ratings (e.g., by performing Acts 2 and 4 on the
provided ratings), then, in Act 5, the other rating
predictabilities may be combined with the rating predictability
generated in Act 4 to produce the rater reputation of the first
entity. Act 5 may be implemented in any of several different ways.
In one implementation, Act 5 may be implemented by averaging all of
the generated rating predictabilities associated with each rating
provided by the first entity. Accordingly, a rater reputation may
be determined by applying the following equation: 14 Equation11: R
rater = 1 n .cndot. j = 1 n P j ( X ) ,
[0093] where n is the number of ratings provided by the first
entity and R.sup.rater is the rater reputation.
[0094] If the rater reputation of the first entity is determined by
averaging the first entity's rating predictabilities, the average
may be a weighted average. For example, each rating predictability
may be weighted as a positive function of the time at which the
rating associated with the rating predictability is provided. For
example, the earlier the associated rating is provided, and thus
the further away in time from when the rater reputation is being
determined, the lower the weighting of the predictability.
Optionally, each rating predictability may be placed in a temporal
order according to the time at which its associated rating was
provided, and each predictability may be weighted according its
position in this order. Further, time may be divided into a number
of intervals, and the temporal intervals may be placed in a
temporal order. A rating predictability then may be weighted
according to the position of the temporal interval in the temporal
order.
[0095] Weighting the rating as such represents the principle that
an entity's rater reputation may change over time, and thus more
recent ratings should be given more weight than older ratings in
determining the rater reputation of an entity. A rater reputation,
determined by applying such a weighted average of predictabilities,
may be determined by applying the following equation: 15
Equation12: R rater = j = 1 n P j ( X ) .cndot. f ( j ) j = 1 n f (
j ) Equation13: f ( j ) = 1 V j ,
[0096] where .function.(j) is a temporal function such as, for
example:
[0097] where V is a constant number having a value<1, and j is
the position of rating predictability, P.sub.j, in a temporal
order, or a time at which the rating associated with the
predictability was provided, or a position in a temporal order of a
temporal interval during which the associated rating was made.
Other weightings and combinations of weightings may be applied to
an average predictability to determine a rater reputation of an
entity such as, for example, a combination of entity reputations
associated with each predictability, described in detail below in
relation to Act 10.
[0098] Determining a rater reputation of a first entity by
averaging rating predictabilities of the first entity may become
more cost prohibitive as the number of ratings of other entities
provided by the first entity increases.
[0099] Accordingly, as an alternative implementation of Act 5, the
rater reputation may be determined recursively. Determining a rater
reputation recursively, for example, as described below, saves
computational space and time, particularly as the number of ratings
of other entities provided by the first entity grows.
[0100] Such a recursive determination may include: providing a
previously determined rater reputation (i.e., an initial rater
reputation) of the first entity; determining a rater reputation
adjustment (which may be a positive or negative value) based on the
generated rating, and adding the reputation adjustments to the
previously determined rater reputation to produce the rater
reputation.
[0101] FIG. 3 is a flow chart illustrating an example
implementation of Act 5. In Act 6, the rating predictability may be
subtracted from the initial rater reputation to produce a
reputation modification. Next, in Act 8, the reputations of the
entities that provided the selected second ratings may be combined
to produce a combined reputation such as, for example, by
calculating an average reputation from these reputations. For
example, a combined reputation may be determined by applying
following equation: 16 Equation14: R combined = u = j + 1 k R u
other k - j ,
[0102] where R.sub.combined is the combined reputation and
R.sub.u.sup.other is the rating of an entity that provided one of
the selected second ratings. Act 8 may be performed in sequence or
concurrently with Acts 2-6.
[0103] Next, in Act 10, the reputation modification may be scaled
by the combined reputation to produce a scaled reputation
modification. This scaling has the effect of weighting the
reputation modification according to the reputations of the
entities corresponding to the second ratings. If the combined
reputation is an average reputation of the entities that provided
the selected second ratings and has a relatively high value, then
the reputation modification is scaled such that the reputation
adjustment is relatively high. Conversely, if the average rater
reputation of these raters is relatively low, then the reputation
adjustment resulting from the scaled reputation modification is
relatively low. The scaled reputation modification may be
determined by applying the following equation: 17 Equation15: R
scaled = R combined [ P ( X ) - R j - 1 rater ] ,
[0104] where 18 R j - 1 rater
[0105] is the initial rater reputation, 19 [ P ( X ) - R j - 1
rater ]
[0106] is the reputation modification, and R.sub.scaled is the
scaled reputation modification.
[0107] Next, in Act 12, the scaled reputation modification maybe
divided by a change rate factor to produce a reputation adjustment.
This change rate factor controls the rate at which the first rating
and the resulting rating predictability can change the rater
reputation of the first entity. For example, if the rate change
factor has a high value, then the rate of change will be slower
such that a relatively higher number of ratings will have to be
provided by an entity before the rater reputation of the entity
approaches an Actual reputation of the entity.
[0108] Next, in Act 14, the reputation adjustment is added to the
initial rater reputation to produce a resulting rater reputation.
The resulting rater reputation may be determined by applying the
following equation: 20 Equation16: R j rater = R j - 1 rater + 1 C
.cndot. R scaled ,
[0109] where 1/C is the change rate factor and R.sub.j.sup.rater is
the resulting rater reputation. The change rate factor is named as
such because its value impacts the rate at which the rater
reputation changes value. Optionally, the value of C may be set
equal to the value of T.sub.o, the effective number of
observations, described below in relation to Equations 19 and
20.
[0110] In an aspect of recursively determining a rater reputation
of an entity, for example, by applying Equation 16, an original
rater reputation (i.e., an entity's rater reputation before a first
rater reputation has been determined for the entity),
R.sub.0.sup.rater, is initialized to zero. Consequently, an
entity's rater reputation has its lowest value before the entity
provides a first rating of another entity. Such an original rater
reputation prevents an entity with a low rater reputation from
creating a new identity as a new entity and beginning with a higher
rater reputation than the low reputation of the entity.
[0111] Recursively determining a rater reputation of a first
entity, for example, by applying Equation 16, may result in an
rater reputation that asymptotically approaches an Actual rater
reputation of the first entity as the number or ratings of other
entities provided by the first entity increases. An Actual rater
reputation of the entity may be determined by averaging the
determined rating predictabilities (and possibly weighting the
average) for each rating provided by the first entity, for example,
by applying Equation 11 or 12, or a variation thereof. As described
above, this averaging may become cost prohibitive as the number of
ratings provided by the first entity increases. Further, this
averaging does not penalize an entity from starting over as a new
entity by initializing to zero an original rater reputation of the
entity. Both of these shortcomings of calculating an Actual
reputation are avoided by initializing to zero an original rater
reputation of an entity and by recursively determining the rater
reputation of the entity, for example, by applying Equation 16 and
variations thereof.
[0112] In an aspect of recursively determining a rater reputation
of an entity, in response to receiving a rating of a rated entity,
the rater reputations of the entities that provided the most recent
M ratings may be determined. In other words, each of the selected
second ratings is one of the most recent M ratings.
[0113] Further, the determined rater reputation for each rating
entity may be weighted as a function of how recent the rating
entity provided its rating in relation to how recent the other
rating entities provided their ratings. For example, for each of
rating entities corresponding to one of the last M ratings
received, the rating entity's rater reputation may be determined by
application of the following equation: 21 Equation 17 : R ( m ) j
rater = R ( m ) j - 1 rater + m M 1 C R ( m ) scaled
[0114] where M is the number of most recent ratings, m is the
number of ratings (of the most recent ratings) received after the
rating received from the rating entity plus 1 (i.e., for the rating
entity that provided the most recent rating, the value of m=1), and
R(m).sub.scaled is the scaled rater reputation modification as
described above in relation to Equation 16, where, for the expected
rating (as described in relation to Equation 8) from which the
scaled rater reputation modification is generated, k-j=m.
[0115] For example, if rater reputations are being determined for
the rating entities that provided the most recent 20 ratings, then,
M=20. For the rating entity that provided the earliest rating, m=20
and m/M=1. Further, for the rating entity that provided the most
recent rating, m=1 and m/M=1/20.
[0116] By weighting the adjustment to the rater reputation of an
entity by m/M, the amount that a rating entity's rater reputation
is adjusted is proportional to the number of ratings received for a
rated entity after the rating provided by the rating entity. This
weighting reflects the principle that if there are more ratings
received after the rating provided by the rating entity, then there
is more information to estimate the predictability of the rating
entity, as described above in relation to Equations 9 and 10, from
which the rater reputation adjustment is determined. This use of
more information results in more accurate estimations of the
predictability of the rating entity, and a more accurate rater
reputation adjustment. Accordingly, by weighting the rater
reputation adjustment as described above in relation to Equation
17, the rater reputation adjustment is weighted as a function of
this accuracy.
[0117] Other techniques may be used for weighting a rater
reputation adjustment as a function of how recent the rating entity
provided its rating in relation to how recent the other rating
entities provided their ratings.
[0118] As described above, in an aspect of determining a rater
reputation of an entity, each time a new entity enters a population
or community of entities that use a rating system incorporating
rater reputation, the new entity may have an original rater
reputation initialized to zero. Initializing an original rater
reputation to zero may be desirable if an entity is capable of
having multiple identities. In a system where an entity is
restricted to a lifetime persistent identity, for example, a social
security number, then the original rater reputation may not be
initialized to zero, but may be initialized to another value. For
example, the original rater reputation may be initialized to an
average rater reputation value.
[0119] In some situations, an entire population of entities that
have been using a rating system to rate each other, and thus have
pre-collected ratings, may want to determine a rater reputation of
one or more, possibly all, entities of the population using these
pre-collected ratings. Such determination of rater reputations for
a population of entities using pre-collected ratings may be
referred to herein as "seeding" a reputation system. If seeding a
reputation system, one or more first entities of the population may
not have provided a sufficient number of ratings of other entities
such that a recursively generated rater reputation of these first
entities approximates an Actual rater reputation of these first
entities, respectively. Consequently, the resulting rater
reputation of one or more first entities that have provided
relatively few ratings is lower than the resulting reputation of
one or more first entities that have provided relatively many
ratings, irrespective of the Actual ratings provided by the first
entities.
[0120] Accordingly, in an aspect of seeding a reputation system by
recursively determining a rater reputation of a first new entity,
pre-collected ratings may be used to determine the rater reputation
as follows. In a first pass of determining the rater reputation,
for example, by applying Equation 16 or variations thereof, the
pre-collected ratings are used and the original rater reputation of
the first new entity may be initialized to zero. A second pass of
determining the rater reputation may use the same pre-collected
ratings and initialize the original rater reputation of the first
new user to the rater reputation resulting from the first pass.
This initialization to a resulting reputation may be repeated in
other passes until the resulting reputation adequately approximates
the Actual rater reputation of the first new entity.
[0121] Further, to seed a reputation system by recursively
determining a rater reputation of a first entity, the expected
rating for each first rating provided by the first entity may be
determined by applying the following equation: 22 Equation 18 : E i
ratee = 1 D i - 1 - C / 2 i - 1 + C / 2 R i - 1 ratee ,
[0122] where R.sub.i-1 is the ratee reputation of the rated entity
at i-1, D is the range of allowed reputation values, 1/C is the
change rate factor and E.sub.i.sup.ratee is the determined expect
rating.
[0123] FIG. 4 is a data flow diagram illustrating an example
embodiment of a system 19 for generating a rater reputation 38. The
rater reputation generator 20 may receive a request 21 from a user
indicating a request for a first entity's reputation. In response
to the user request 21, the rater reputation generator 20 may
receive as input a first rater rating 26 and selected second
ratings 28, and generate the resulting rater reputation 38 as
output, for example, by performing Acts 2-5 of FIG. 2. In one
implementation, the rater reputation generator 20 may also receive
as input other rating predictabilities 49 to generate a resulting
rater reputation 38 by averaging rating predictabilities, as
described above in relation to FIG. 2.
[0124] In another implementation, the rater reputation generator 20
may also receive as input an initial rater reputation 22, other
rater reputations 24, a ratee reputation deviation 30, a range 32,
and a change rate factor 34, and use these inputs in addition to
inputs 26 and 28 to generate a resulting rater reputation 38 such
as, for example, by applying Acts 6-14 of FIG. 3.
[0125] In yet another implementation, the rater reputation
generator further may receive a value 31 indicating a number of
most recent ratings and a value 33 indicating the number of ratings
provided for the rated entity after the first rater rating 26 was
provided. Values 31 and 33 correspond to values M and m,
respectively described above in relation to Equation 17.
[0126] FIG. 5 is a data flow diagram illustrating a more detailed
example embodiment of the system 19 for generating a resulting
rater reputation 38. The rater reputation generator 20 may include
an expected rating generator 40, a predictability generator 44, a
reputation modification generator 48, a combined predictability
generator 47, a combined reputation generator 52, a reputation
adjustment generator 56 and an adder 60.
[0127] In response to the rater reputation generator 20 receiving
the user request 21, the expected rating generator 40 may receive
as input the selected second ratings 28 and generate as output an
expected rating 42. The expected rating generator 40 also may
receive as input other rater reputations 24, and use this input 24
in addition to input 28, to generate the expected rating 42. The
other rater reputations 24 may be the reputations of the entities
that provided the selected second ratings 28. Expected rating
generator 40 may generate expected ratings 42 in accordance with
the various techniques described above in relation to Act 2 of FIG.
2.
[0128] The predictability generator 44 may receive as input the
expected ratings 42 and the first rater rating 26 and produce a
rating predictability 46 as output. The predictability generator 44
also may receive the ratee reputation deviation 30 and the range
32, and use both of these inputs to help produce the rating
predictability 46. The predictability generator 44 may generate the
rating predictability 46 in accordance with the various techniques
discussed above in relation to Act 4 of FIG. 2.
[0129] The rater reputation of a first entity may be determined
according to Equations 11 or 12 by including in the rater
reputation generator the combined predictability generator 47 that
receives as input rating predictability 46 and other rating
predictabilities 49 (which may have been generated by
predictability generator 44) and generates as output a resulting
rater reputation 38.
[0130] The reputation modification generator 48 may receive a
rating predictability 46 and an initial rater reputation 22, and
generate a reputation modification 50. The reputation modification
generator 48 may generate the reputation modification 50 by
subtracting the rating predictability from the initial rater
reputation as described above in relation to Act 6 of FIG. 3.
[0131] The combined reputation generator 52 may receive other rater
reputations 24 and generate a combined reputation value 54. The
combined reputation generator 52 may calculate the average of the
other rater reputations to determine the combined reputation 54. A
reputation adjustment generator 56 may receive the combined
reputation 54 and the reputation modification 50 and generate a
reputation adjustment 58. The reputation adjustment generator 56
may also receive the change rate factor 34. The reputation
adjustment generator 56 may generate the reputation adjustment 58
in accordance with the techniques described above in relation to
Acts 10 and 12 of FIG. 3. As described above, the combined
reputation 54 may scale the reputation modification 50 in
accordance with the combined reputation 54 of the other raters.
[0132] In an embodiment, the reputation adjustment generator
further receives values 31 and 33 corresponding to values M and m,
respectively, and generates the rater reputation adjustment 58 as
described above in relation to Equation 17.
[0133] The adder 60 may add the initial rater reputation 22 to the
reputation adjustment 58 to produce the resulting rater reputation
38.
[0134] The rater reputation generator 20 and any components
thereof, including expected rating generator 40, predictability
generator 44, combined reputation generator 52, combined
predictability generator 47, reputation modification generator 48,
reputation adjustment generator 56 and adder 60, may be implemented
as software, hardware, firmware or any combination thereof. The
rater reputation generator 20 and any components thereof may reside
on a single machine (e.g., a computer), or may be modular and
reside on multiple interconnected (e.g., by a network) machines.
Further, on each of the one or machines that include the rater
reputation generator 20 or a component thereof, the generator 20 or
component may reside in one or more locations on the machine. For
example, different portions of the rater reputation generator 20 or
different portions of a component may reside in different areas of
memory (e.g., RAM, ROM, disk, etc.) on a computer.
[0135] The range 32 and the change rate factor 34 may be constants
for the system 19 and may be stored in a reputation database or
other data structure, as described in more detail below in relation
to FIG. 18. The first rater rating 26, the other reputations 24,
the selected second ratings 28, the ratee reputation deviation 30,
the initial rater reputation 22, and values 31 and 33 also may be
stored in the reputation database or other structure. In response
to receiving the user request 21, the rater reputation generator 20
may access the reputation database or similar data structure and
retrieve values 26, 32, 24, 28, 26, 30, 31, 32, 33 and 34 to
generate the resulting rater reputation 38 as described above. The
resulting rater reputation 38 then may be stored in the reputation
database or other data structure for later access.
[0136] System 19, and the components thereof, are merely example
embodiments of a system for generating a rater reputation. Such
example embodiments are not meant to limit the scope of the
invention and are provided merely for illustrative purposes, as any
of a variety of other systems and components for determining a
rater reputation, determining a may fall within the scope of the
invention.
[0137] II. Determining a Ratee Reputation Deviation
[0138] FIG. 6 is a data flow diagram illustrating an example
embodiment of a system 69 for recursively generating the ratee
reputation deviation 30 of a rated entity. Similarly to as
described above in relation to recursively determining a rater
reputation, determining a ratee reputation deviation recursively
saves computational space and time, particularly as the number of
provided ratings for the rated entity grows.
[0139] The ratee reputation deviation generator 72 may receive as
input a ratee reputation 70, a rater reputation 74, an initial
ratee reputation deviation 75, a most recent rating 76 and a
forgetting factor 77. The ratee reputation deviation generator 72
may also receive the range 32.
[0140] The ratee reputation 70 is the ratee reputation determined
from the most recent rating 76 of the rated entity, for example, by
applying Equation 1 as disclosed in the Zacharia thesis, or by
applying a variation of Equation 1, where the rater reputation is
determined in accordance with Equation 15 or a variation thereof,
as described above in relation to FIG. 2. The initial ratee
reputation deviation 75 is the ratee reputation deviation of the
rated entity before the most recent rating 76 was provided. The
rater reputation 74 is the rater reputation of an entity that
provided the most recent rating 76. The ratee reputation generator
72 may generate from these inputs the ratee reputation deviation 30
that represents a reliability of the rated entity's ratee
reputation.
[0141] Optionally, the ratee reputation deviation generator 72 also
may receive a minimum allowed rater reputation deviation and
compare it to the determined rater reputation deviation 30. If the
minimum deviation is greater than deviation 30, then the rater
reputation deviation 30 may be set equal to the minimum allowed
rater reputation.
[0142] Accordingly, the ratee reputation deviation generator 72 may
generate the ratee reputation deviation 30 by applying the
following equation: 23 Equation 19 : ( R D i ratee ) 2 = max ( [ R
D min ratee ] 2 , F ( R D i - 1 ratee ) 2 + ( R i rater ( W i - E i
ratee , past ) ) 2 / T o ) ,
[0143] where 24 R D min ratee
[0144] is a minimum allowed ratee reputation deviation, F is the
forgetting factor 172, 25 R D i - 1 ratee
[0145] is the initial ratee reputation 75, R.sub.i.sup.rater is the
rater reputation 74, W.sub.i is the most recent rating 76, T.sub.0
is an effective number of reputation determinations,
E.sub.i.sup.rater,past is the expected rating, and
RD.sub.i.sup.ratee is the resulting ratee reputation deviation 30.
The expected rating may be calculated by applying Equation 2 as
described in the Zacharia thesis. The forgetting factor is a
constant having a value<1 such that older ratings have less
weight in determining the resulting ratee reputation deviation than
more recent ratings. T.sub.0 may be derived from F by applying the
following equation: 26 Equation 20 : T o = i = 0 .infin. F i = 1 1
- F .
[0146] Optionally, the effective number of determined reputations
may be used to determine the change rate factor described above by
setting C=T.sub.0.
[0147] Other statistical methods may be used to determine a
reliability of a ratee reputation. For example, a forgetting factor
may not be applied, or an average deviation may be determined for
all determined ratee reputations of the rated entity, or such an
average deviation may be calculated with each ratee reputation
weighted according to how recent the ratee reputation was
determined.
[0148] In an aspect of recursively determining a ratee reputation
deviation of a rated entity, for example, by applying Equation 19,
an original ratee reputation deviation, RD.sub.i.sup.ratee, is
initialized to a maximum allowed value for a ratee reputation
deviation. Such a maximum value may be predetermined. Consequently,
a rated entity's ratee reputation deviation has its highest value
before the rated entity is rated by other entities. Assigning a
maximum value to an original ratee reputation deviation prevents an
entity with a high ratee reputation deviation from creating a new
identity as a new entity and beginning with a more desirable lower
ratee reputation deviation.
[0149] Equation 19 may be considered a recursive estimation
algorithm of Recursive Least Squares (RLS) with a forgetting factor
of F. Equation 19 estimates recursively an average square deviation
of an actual rating from an expected (i.e., estimated) rating
described in more detail below in relation to FIGS. 8 and 9. For
more information regarding Recursive Least Squares, please refer to
Chapter 9 of "Lecture Notes and Non-Linear and Non-Stationary Time
Series Analysis," by H. Madsen and J. Holst, Institute of
Mathematical Modeling (IMM), Technical University of Denmark,
Lyngby, Denmark, 1998 (hereinafter the Madsen text), the contents
of which is herein incorporated by reference in its entirety.
[0150] Other statistical methods may be used to determine a
reliability of a ratee reputation. For example, a forgetting factor
may not be applied, or an average deviation may be determined for
all determined ratee reputations of the rated entity, or such an
average deviation may be calculated with each ratee reputation
weighted according to how recent the ratee reputation was
determined.
[0151] The ratee reputation deviation generator 72 may be
implemented as software, hardware, firmware or any combination
thereof. The ratee reputation deviation generator 72 may reside on
a single machine (e.g., a computer), or may be modular and reside
on multiple interconnected (e.g., by a network) machines. Further,
on each of the one or more machines that include the ratee
reputation deviation generator 72 or modules thereof, the generator
72 or modules may reside in one or more locations on the machine.
For example, different portions of the ratee reputation deviation
generator 72 or modules may reside in different areas of memory
(e.g., RAM, ROM, disk, etc.) on a computer.
[0152] The range 32 and the forgetting factor 77 may be constants
stored in a reputation database or similar data structure as
described in more detail in relation to FIG. 18. The ratee
reputation 70, the rater reputation 74, and the initial ratee
reputation deviation 75 also may be stored in the reputation
database or similar structure. The ratee reputation deviation
generator 72, in response to receiving the most recent rating 76,
may access the reputation database or similar structure to generate
the ratee reputation deviation 30 as described above. The ratee
reputation deviation 30 then may be stored in the reputation
database or similar structure for later access.
[0153] System 69 is merely an example embodiment of a system for
generating a ratee reputation deviation. Such an example embodiment
is not meant to limit the scope of the invention and is provided
merely for illustrative purposes, as any of a variety of other
systems for determining a ratee reputation deviation may fall
within the scope of the invention.
[0154] III. Determining a Rater Reputation Deviation
[0155] In addition to having an estimate of the reliability of the
ratee reputation, for example, the ratee reputation deviation 30,
it may be desirable to have an estimate of the reliability of the
rater reputation of an entity that is providing a rating. Such a
reliability may be estimated by calculating of a rater reputation
deviation. Similarly to as described above in relation to ratee
reputation deviation, computational space and time may be saved by
calculating a rater reputation deviation recursively, particularly
as the number of ratings of other entities provided by the first
entity increases.
[0156] Accordingly, a rater reputation deviation may be determined
recursively by applying the following equation: 27 Equation 21 : (
RD j rater ) 2 = max ( [ R D min rater ] 2 , F ( R D j - 1 rater )
2 + R scaled 2 / T 0 ) ,
[0157] where 28 R D min rater
[0158] is a minimum allowed rater reputation deviation, 29 R D j -
1 rater
[0159] is an initial rater reputation deviation, RD.sub.j.sup.rater
is the resulting rater reputation deviation, and the other symbols
are as described above.
[0160] Equation 21, and variations thereof, estimate recursively an
average square deviation of a rater reputation of an entity from an
expected (i.e., estimated) rater reputation. For each recursive
estimate, the initial rater reputation deviation, 30 R D j - 1
rater ,
[0161] may be weighted by the forgetting factor, F, and the average
square deviation may be divided by the effective number of
determined reputations, T.sub.0. Such recursive estimation is
described in more detail in chapter 9 of the Madsen text.
[0162] Other statistical methods may be used to determine a
reliability of a rater reputation. For example, a forgetting factor
may not be applied, or an average deviation may be determined for
all determined rater reputations of the rating entity, or such an
average deviation may be calculated with each rater reputation
weighted according to how recent the rater reputation was
determined.
[0163] In an aspect of recursively determining a ratee reputation
deviation of a first entity, for example, by application of
Equation 21, an original rater reputation deviation,
RD.sub.j.sup.rater, is initialized to a maximum value. This maximum
value may be predetermined. Consequently, a first entity's rater
reputation deviation has its highest value before the first entity
provides a first rating of another entity. Assigning a maximum
value for the rater reputation deviation prevents a first entity
with a high rater reputation deviation from creating a new identity
as a new entity and beginning with a lower rater reputation
deviation.
[0164] FIG. 7 is a data flow diagram illustrating an example
embodiment of a system 79 for generating a rater reputation
deviation. A rater reputation deviation generator 82 may receive as
input an initial rater reputation 22, a rating predictability 46,
an initial rater reputation deviation 80 and a forgetting factor
77. Rater reputation deviation generator 82 also may receive other
rater reputations 24.
[0165] The rater reputation deviation generator 82 may generate the
rater reputation deviation 84 from inputs 22, 24, 46 and 80.
Alternatively, in place of inputs 22, 24 and 46, deviation
generator 82 may receive the scaled reputation modification as
input and generate the rater reputation deviation 84 in accordance
with Equation 21 or variations thereof.
[0166] Optionally, the rater reputation deviation generator 82 may
receive a minimum allowed rater reputation deviation, 31 RD min
rater ,
[0167] and compare it to the determined rater reputation deviation
84. If the minimum deviation is greater than deviation 84, then the
rater reputation deviation 84 may be set equal to the minimum
allowed rater reputation.
[0168] The rater reputation deviation generator 82 may be
implemented as software, hardware, firmware or any combination
thereof. The rater reputation deviation generator 82 may reside on
a single machine (e.g., a computer), or may be modular and reside
on multiple interconnected (e.g., by a network) machines. Further,
on each of the one or more machines that include the rater
reputation deviation generator 82 or modules thereof, the generator
82 or modules thereof, the generator 82 or modules may reside in
one or more locations on the machine. For example, different
portions of the rater reputation deviation generator 82 may reside
in different areas of memory (e.g., RAM, ROM, disk, etc.) on a
computer.
[0169] The forgetting factor 77 may be stored as a constant in a
reputation database or similar data structure as described below in
relation to FIG. 18. The initial rater reputation 22, the rating
predictability 46, the other rater reputations 24 and the initial
rater reputation deviation 80 also may be stored in the reputation
database or similar data structure. In response to receiving the
rating predictability 46, the rater reputation deviation generator
82 may access the reputation database or similar data structure to
access and retrieve values 22, 24, 80 and 77, and generate rater
reputation deviation 84. The rater reputation deviation 84 then may
be stored in the reputation database or similar structure for later
access.
[0170] System 79 is merely an example embodiment of a system for
generating a rater reputation deviation. Such an example embodiment
is not meant to limit the scope of the invention and is provided
merely for illustrative purposes, as any of a variety of other
systems and components for determining a rater reputation deviation
may fall within the scope of the invention.
[0171] IV. Determining a Ratee Reputation Using a Rater
Reputation
[0172] If a rater reputation is determined for a first entity, and
the first entity provides a first rating of a rated entity, the
rater reputation and the first rating may be used to determine, at
least in part, a ratee reputation of the rated entity.
[0173] Accordingly, provided herein is a method and system for
determining a ratee reputation of a rated entity based at least in
part on one or more ratings of the rated entity provided by one or
more entities and the rater reputations of these one or more
entities.
[0174] In one embodiment of determining a ratee reputation, the
ratee reputation of a rated entity is determined by averaging all
of the ratings provided by other entities for the rated entity, and
by weighting each rating with the rater reputation of the entity
that provided the rating, to produce a weighted average.
Accordingly, a ratee reputation may be determined by applying the
following equation: 32 Equation22: R ratee = j = 1 n [ W j .cndot.
R j rater ] j = 1 n R j rater ,
[0175] where W.sub.j is a rating supplied, R.sub.j.sup.rater is the
rater reputation of the entity who provided the rating W.sub.j.
[0176] In generating such a weighted average, each rating may be
further weighted with a positive function of the time at which the
rating was provided. For example, the earlier the rating is
provided, and thus the further away in time from when the ratee
reputation is being determined, the lower the weighting of the
rating. Optionally, each rating may be placed in a temporal order
according to the time at which the rating was provided, and each
rating may be weighted according its position in this order.
Further, time may be divided into a number of intervals, and the
temporal intervals may be placed in a temporal order. A rating then
may be weighted according to the position of the temporal interval
in the temporal order.
[0177] Similar to as described above in relation to determining a
rater reputation, temporally weighting each rating represents the
principle that an entity's ratee reputation may change over time.
Thus, more recent ratings should be given more weight than older
ratings in determining the ratee reputation of an entity.
Accordingly, a ratee reputation may be determined by applying the
following equation: 33 Equation23: R ratee = j = 1 n [ W j .cndot.
R j rater .cndot. f ( j ) ] j = 1 n R j rater .cndot. j = 1 n f ( j
) ,
[0178] where .function.(j) is a temporal function such as, for
example, Equation 12, above. Other weightings and combinations of
weightings may be applied to a rating to determine a ratee
reputation of an entity.
[0179] Determining a ratee reputation of an entity by averaging
ratings may become more cost prohibitive as the number of ratings
provided by other entities for the first entity increases.
[0180] Accordingly, in another embodiment of determining the ratee
reputation of an entity, the ratee reputation may be determined
recursively. Similarly to as described above in relation to
determining a rater reputation, determining the ratee reputation
recursively saves computational space and time, particularly as the
number of ratings provided for the rated entity increases.
[0181] A recursive determination may include: providing a
previously determined ratee reputation (i.e., an initial ratee
reputation) of the entity; determining a ratee reputation
adjustment (which may be a positive or negative value) based on a
received rating, and adding the reputation adjustments to the
previously determined rater reputation.
[0182] FIG. 8 is a flow chart illustrating an example
implementation of a method of recursively determining a ratee
reputation of an entity. In Act 90, an expected rating may be
determined from the range and the ratee's initial reputation. An
expected rating may be determined by applying Equation 4 or a
variation thereof.
[0183] Next, in Act 92, the expected rating may be subtracted from
the first rating to produce a ratee reputation modification. The
ratee reputation modification may be produced as described in
relation to Equation 23 below.
[0184] Next, in Act 94, the ratee reputation modification may be
scaled by a rater reputation of the rating entity to produce a
scaled ratee reputation modification. This rater reputation may be
the resulting rater reputation generated according to one of the
methods described in relation to FIGS. 2 and 3, or a variation
thereof.
[0185] Combining Acts 92 and 94, the scaled ratee reputation
modification may be produced by applying the following
equation:
R.sub.i.sup.scaled=R.sub.i.sup.rater.multidot.(W.sub.i-E.sub.i),
Equation 24
[0186] where R.sub.i.sup.rater is the rater reputation, W.sub.i is
the first rating, E.sub.i is the expected rating, and
W.sub.i-E.sub.i is the ratee reputation modification.
[0187] Next, in Act 96, the scaled ratee reputation modification
may be damped by a damping factor to produce a damped ratee
reputation modification. The damped ratee reputation modification
may be produced by applying the following equation: 34 Equation25:
R i damped = damp ( R i - 1 ratee ) .cndot. R i scaled ,
[0188] where 35 R i - 1 ratee
[0189] is the initial ratee reputation, and 36 damp ( R i - 1 ratee
)
[0190] is the damping factor. Optionally, the damping factor may be
determined (in series or concurrently with Acts 90-94) by applying
Equation 2, where the acceleration factor, a, may be predetermined
to control the rate at which the ratee reputation can change.
[0191] In a next Act 98, the damped ratee reputation modification
may be divided by a change rate factor to produce a ratee
reputation adjustment. The change rate factor affects the rate at
which the ratee reputation may change.
[0192] Next, in Act 100, the ratee reputation may be added to the
initial ratee reputation to produce a resulting ratee reputation.
In accordance with Acts 98 and 100, the resulting ratee reputation
may be determined by applying the following equation: 37
Equation26: R i ratee = R i - 1 + 1 C .cndot. R i damped ,
[0193] where C is the change rate factor and R.sub.i.sup.ratee is
the resulting ratee reputation.
[0194] In an aspect of recursively determining a ratee reputation
of an entity using one or more rater reputations, for example, by
applying Equation 26, an original ratee reputation,
R.sub.0.sup.rater, for the entity has its lowest value before any
ratings are provided for the entity. Assigning a lowest value to an
original ratee reputation prevents an entity with a low ratee
reputation from creating a new identity as a new entity and
beginning with a higher ratee reputation.
[0195] Other methods of determining a ratee reputation may be
used.
[0196] A ratee reputation of a first entity may be used for a
variety of purposes, including determining whether to transact with
the first entity, determining a price to pay for a good or service
of the first entity and determining a price to pay for insuring a
quality of a good or service of the first entity.
[0197] FIG. 9 is a data flow diagram illustrating an example
embodiment of a system 109 for generating a resulting ratee
reputation 140. A ratee reputation generator 110 may receive an
acceleration factor 112, a range 32, an initial ratee reputation
114, a first rating 116, a rater reputation 118 and a change rate
factor 120, and generate a resulting ratee reputation 140 as
output.
[0198] The ratee reputation generator 110 may include a damping
factor generator 130, an expected rating generator 122, a ratee
reputation modification generator 126, a ratee reputation
adjustment generator 134 and an adder 138.
[0199] The expected rating generator 122 may receive as input the
range 32 and the initial ratee reputation 114, and generate an
expected rating 124, for example, by applying Equation 3. The ratee
reputation modification generator 126 may receive the first rating
116 and the expected rating 124, and generate the ratee reputation
modification 128, for example, as described above in relation to
Equation 23.
[0200] The damping factor generator 130 may receive the
acceleration factor 112, the initial ratee reputation 114 and the
range 32, and generate the damping factor 132, for example, by
applying Equation 3 above.
[0201] The ratee reputation adjustment generator 134 may receive
the ratee reputation modification 128, the damping factor 132, the
rater reputation 118 and the change rate factor 120 and generate
the ratee reputation adjustment 136, for example, by applying
Equation 25 above.
[0202] The adder 138 may receive the initial ratee reputation 114
and the ratee reputation adjustment 136 and generate the resulting
ratee reputation 140, for example, by applying Equation 26
above.
[0203] Although not shown in FIG. 9, the system 109 also may
include a transaction module such as transaction module 528
described below in relation to FIG. 11. The transaction module may
receive the resulting ratee reputation 140, determine whether to
transact with the rated entity based on the resulting ratee
reputation, and then output a value, for example, a boolean value,
which indicates whether or not to transact with the rated
entity.
[0204] The ratee reputation generator 110, and any combination of
its components 110, 122, 126 and 134 may be implemented using
software, firmware or hardware, or any combination thereof. The
ratee reputation generator 110 and any components thereof may
reside on a single machine (e.g., a computer), or may be modular
and reside on multiple interconnected (e.g., by a network)
machines. Further, on each of the one or machines that include the
ratee reputation generator 110 or a component thereof, the
generator 110 or component may reside in one or more locations on
the machine. For example, different portions of the ratee
reputation generator 110 or different portions of a component may
reside in different areas of memory (e.g., RAM, ROM, disk, etc.) on
a computer.
[0205] The acceleration factor 112, the range 32 and the change
rate factor 120 may be stored as constants in a reputation database
or similar data structure as described below in relation to FIG.
18. The initial ratee reputation 114 and the rater reputation 118
may also be stored in the reputation database or similar data
structure. In response to receiving the first rating 116, the ratee
reputation generator may access the reputation database and
retrieve values 112, 32, 114, 116, 118 and 120 to generate the
resulting ratee reputation 140 as described above. The resulting
ratee reputation 140 then may be stored in the reputation database
or similar structure for later access.
[0206] System 109, and components thereof, are merely example
embodiments of a system for generating a ratee reputation. Such
example embodiments are not meant to limit the scope of the
invention and are provided merely for illustrative purposes, as any
of a variety of other systems and components for determining a
ratee reputation may fall within the scope of the invention.
[0207] V. Determining a Reputation of an Entity From a Perspective
of Another Entity
[0208] FIG. 10 is a flowchart illustrating an example embodiment of
a method for determining a personalized ratee reputation. In a
first Act 402, a breadth-first search is performed beginning at the
second entity to determine, from one or more rating paths, one or
more first rating paths that have a first length. In a next Act
404, for each determined first rating path, a third entity that has
a level equal to or less than the first length is identified.
[0209] In a next Act 406, for each identified third party, a first
rating of the first identity provided by the third entity is
determined. For each identified third entity, it may be determined
that the third entity has provided more than one rating of the
first entity. For each third entity for which it has been
determined that more than rating has been provided, a most recent
rating may be selected from the one or more ratings to serve as the
first rating of the first entity provided by the third entity.
[0210] In a following Act 408, the first ratings are combined. The
first ratings may be combined in any of a variety of ways. In one
embodiment, an average of the first ratings is calculated.
Calculating this first average may include, for each first rating,
weighting the first rating as a function of a personalized ratee
reputation of the corresponding third entity from the prospective
of the second entity. This weighting may be relative to
personalized ratee reputations of the other third entities from the
perspective of the second entity.
[0211] The personalized ratee reputation of the third entity may be
determined in any of a variety of ways. In one embodiment, one or
more fourth entities that are on the first rating paths, that have
provided a second rating of the third entity and that have a level
equal to one less than the level of the third entity are
determined. The second ratings provided by these one or more fourth
entities are then combined to produce the personalized ratee
reputation of the third entity from the perspective of the second
entity. Combining the second ratings may include calculating an
average of the second ratings. Optionally, Equation 4 or a
variation thereof may be applied to combine the ratings. 38
Equation27: R k ( n ) = D .cndot. [ R j ( n - 1 ) .cndot. W jk ( n
) ] .cndot. f ( n ) R j ( n - 1 ) ,
[0212] In a next Act 410, the personalized ratee reputation is
produced by weighting the combined first rating as a function of
the first length. Act 410 may be performed by application of the
following Equation:
[0213] where R.sub.k(n) is the personalized ratee reputation of an
entity k from a perspective of a second entity a distance n from
the entity k, W.sub.jk(n) is a rating provided by an entity j for
the entity k, where the entity j is a distance n-1 from the second
entity, R.sub.j(n-1) is the personalized ratee reputation of the
entity j from the perspective of the second entity, D is a range of
allowable personalized ratee reputation valves, and f(n) is a
function of the distances n between the second entity and the
entity K (i.e., a function of the length of the rating paths
between the second entity and entity K), such as, for example: 39
Equation28: f ( n ) = 1 T n
[0214] where T is a constant number having a value>1.
[0215] This personalized ratee reputation may then be used to
determine whether to transact with the first entity.
[0216] Weighting the personalized ratee reputation as a function of
the distance between the first and second entity, where the shorter
distance the greater the value of the weighting represents the
principle that the less attenuated the rating path between a first
and second entity, the more likely the second entity is to trust
the determined personalized ratee reputation.
[0217] A personalized ratee reputation of a first entity may be
used for a variety of purposes, including determining whether to
transact with the first entity, determining a price to pay for a
good or service of the first entity and determining a price to pay
for insuring a quality of a good or service of the first
entity.
[0218] FIG. 11 is a data flow diagram illustrating an example
embodiment of a system 500 for determining a personalized ratee
reputation of a first entity from the perspective of a second
entity. The system 500 may include a personalized ratee reputation
generator 506 and a transaction module 528.
[0219] The personalized ratee reputation generator 506 may include
a path-searching module 508, a first look-up module 514, a
processing module 512, a second look-up module 520 and a ratings
combiner 524.
[0220] The path-searching module 508 may receive an indication of
the first entity 502, an indication of the second entity 504 and
other ratings 517, and generate a signal indicating first rating
paths 510. The path-searching module may determine the first rating
paths 510 as described above in relation to Act 402 of FIG. 10. The
path-searching module 508 may determine the first rating path 510
by searching or looking-up rating values in a ratings database or
reputation database as described in more detail below in connection
to FIG. 18.
[0221] The first look-up module 514 may receive the indication of
the first rating paths 510 and generate third entities 518, for
example, as described above in connection to Act 404. The first
look-up module 514 may determine the third entities 518 by looking
up the third entities in a ratings or reputation database as
described in more detail in relation to FIG. 18.
[0222] The second look-up module 520 may receive the indication of
the third entity 518 as input and generate as output the first
ratings 522, for example, as described above in relation to Act
406. The second look-up module 520 may determine the first ratings
522 by looking up the first ratings in a ratings or reputation
database, using the third entity 518 as a look-up criteria, as
described in more detail below in relation to FIG. 14.
[0223] The processing module 512 receives the indication of the
first rating paths 510 and the indication of the third entities 518
and generates second ratings 516. The processing module 512 may
generate the second ratings 516 as described above in relation to
Act 410. The processing module 512 may determine the second ratings
516 by looking up values in the ratings or reputation database
described below in relation to FIG. 14, using the first rating
paths 510 and the third entities 518 as search criteria.
[0224] The ratings combiner 524 receives the first ratings 522 and
the second ratings 516 and generates the personalized ratee
reputation 526. The ratings combiner 524 may also receive other
ratings 517, which may have been generated in a similar manner to
the first ratings 522 and the second ratings 516, for example, by
applying Equation 27. The ratings combiner 524 may generate the
personalized ratee reputation 526 as described above in relation to
Act 410, and then may store the personalized ratee reputation in
the reputation database or similar structure for later access.
[0225] The transaction module 528 may receive the personalized
ratee reputation 526, determine whether to transact with the first
entity based on the personalized ratee reputation, and then output
a value, for example, a Boolean value 530, which indicates whether
or not to transact with the first entity. The boolean rating 530
then may be stored in the reputation database or similar structure
for later access.
[0226] The transaction module 528, the personalized reputation
generator 506 and any component thereof, including the path
searching module 508, the first lookup module 514, the second
lookup module 520, the processing module 512 and the ratings
combiner 524, may be implemented as software, hardware, firmware or
any combination thereof. The module 528, generator 506 and
components thereof may reside on a single machine (e.g., a
computer), or may be modular and reside on multiple interconnected
(e.g., by a network) machines. Further, on each of the one or more
machines that include the transaction module 528, personalized
reputation generator 506 or a component thereof, the generator 506,
transaction module 528 or component may reside in one or more
locations on the machine. For example, different portions of the
personalized reputation generator 506, different portions of a
component, and different portions of the transaction module 528 may
reside in different areas of memory (e.g., RAM, ROM, disk, etc.) on
a computer.
[0227] System 500, and components thereof, are merely example
embodiments of a system for generating a personalized reputation
generator and transacting based on the personalized reputation
generator 506. Such example embodiments are not meant to limit the
scope of the invention and are provided merely for illustrative
purposes, as any of a variety of other systems and components for
generating a personalized ratee reputation and transacting based
thereon may fall within the scope of the invention.
[0228] VI. Determining a Temporal Estimate of the Impact of One or
More Attributes on an Overall Attribute or Result
[0229] The methods and systems described above for determining a
rater reputation, a rater reputation deviation, a ratee reputation,
a ratee reputation deviation and a personalized ratee reputation
are all described in the context of a single rating being provided
for each exchange between a rating entity and a rated entity.
[0230] Alternatively, in an aspect of rating an exchange, multiple
ratings may be provided. For example, in an exchange involving the
sale of a good, the rating entity may provide a rating for the
quality of the good, a rating for the service quality, a rating for
the promptness of delivery and an overall rating for the exchange.
If such multiple ratings are received for a single exchange, the
systems and methods described above for determining reputations and
deviations may be applied to each rating independently. For
example, the rater reputation and rater reputation deviation of a
rating entity as a rater of the quality of the good may be
determined or the ratee reputation and ratee reputation deviation
of a rated entity for the promptness of its delivery of goods may
be determined.
[0231] Further, if multiple ratings are received for a single
exchange, and an overall rating is provided or a result based on
the transaction occurs, it may be desirable to determine the impact
of the one or more ratings on the overall rating or the result. It
further may be desirable to determine the impact of these one or
more ratings on an overall rating or results over time as ratings
are received.
[0232] Accordingly, a method and system for recursively estimating
the impact of one or more attributes, e.g., ratings, on a result
corresponding to a qualitative assessment, e.g., an overall rating,
where the qualitative assessment is based on the one or more
attributes, will now be described. Determining such a recursive
estimate over time allows the impacts of one or more ratings to be
incrementally corrected as more ratings are received.
[0233] Although recursively estimating the impact of one or more
attributes on a result is described below mostly in the context of
ratings, the impacts of other types of attributes may be used. For
example, one or more objective metrics may be assigned according to
the quality of a good, the time of delivery, the cost of the good,
etc., and the impact of this metric on a result or qualitative
assessment may be determined.
[0234] A result of a qualitative assessment may be the qualitative
assessment itself such as, for example, feedback, a rating or a
score, or may be an action taken as a result of a qualitative
assessment such as setting a price or making a selection. For
example, if several entities purchase a good from a seller, and
each entity provides separate ratings for the quality of the good,
the service quality and the promptness of delivery, and also
provides an overall rating of the seller, it may be desirable to
determine the impact of each rating on the overall rating.
Subsequently, the impact of each rating may be applied to estimate
the impact of future ratings on a future overall rating.
[0235] It may be desirable to determine recursively a temporal
estimate of the impact of one or more attributes of a plurality of
attributes in any of the following scenarios:
[0236] a) Determining an overall rating;
[0237] b) Setting a price of a narrowly defined service or
item;
[0238] c) Determining a frequency (i.e., a selectivity ratio) that
a particular entity, item or service is selected when compared with
other entities items or services; and
[0239] d) Determining any other rating aspect, score, or other
quality assessment for an entity, item, service, or policy that can
be reduced to a numerical or Boolean state.
[0240] FIG. 12 is a flow chart illustrating an example embodiment
of a method 800 for estimating the impacts of a plurality of
ratings on a result. In a first Act 802, a plurality of ratings
provided for a first entity by a second entity in association with
an exchange between the first and second entity are received. Each
of these ratings corresponds to a respective rating parameter for
the first entity. For example, rating parameters may include
quality of service, speed of delivery and quality of a good, and
the corresponding ratings may be 0.5, 0.7 and 0.2,
respectively.
[0241] In a next Act 804, a result based on the exchange between
the first and second entity may be received. This result may
correspond to a result parameter for the first entity. For example,
the result parameter may be an overall rating or a price, and the
corresponding result may be 0.7 or $7.95, respectively.
[0242] In a following Act 806, for each received rating, a
corresponding initial weighting value that corresponds to the
rating parameter of the received rating is accessed. This initial
weighting represents an estimated weight of the received rating for
the rating parameter relative to received ratings for the other
rating parameters in determining the result for the result
parameter. For example, historically, the received ratings for the
quality of a good sold by a first entity may have more weight in
determining a price of the good than ratings received for the speed
of delivery. For example, the speed of delivery may have a
corresponding initial weighting value of 0.6, and the quality of
the good may have a corresponding initial weighting value of
0.4.
[0243] Next, in Act 808, an estimated result is determined by
applying each initial weighting value to its corresponding receive
rating. For example, if a rating of 0.6 is received for the speed
of delivery and the rating of 0.4 is received for the quality of
the good, and the speed of delivery has an initial weighting value
0.3 and the quality of the good has an initial weighting value of
0.7, then the estimated result may be determined as
(0.6)(0.3)+(0.4)(0.7)=0.46.
[0244] In a next Act 810, the estimated result may be compared to
the received results to produce a weighting modification. For
example, if the estimated result is 0.46, but the actual received
result is 0.054, then the weighting modification may be
0.54-0.46=0.08.
[0245] In a following Act 812, for each initial weighting value,
the initial weighting value may be adjusted as a function of the
weighting modification to produce a resulting weighting value for
the corresponding rating parameter. For example, the weighting
modification may be added to each initial weighting value.
[0246] In an aspect of generating a resulting weighting value, the
initial weighting values may be adjusted as a function of previous
ratings provided for the rating parameters, for example, as
described in more detail below in connection to FIG. 14.
[0247] FIG. 13 is a data flow diagram illustrating an example
embodiment of a system for estimating the impacts of a plurality of
ratings on a result. The system 169 may include a ratings database
205 and a weightings generator 170 that includes a weighting
modification generator 194 and a weighting adjuster 195. The
ratings database 205 may be a reputation database or similar data
structure such as described below in relation to FIG. 18.
[0248] The weighting modification generator 194 may receive ratings
175 and a result 178, access initial weighting values 179
corresponding to the ratings 175, and generate the weighting
modification 196, as described above in Acts 802-810 of FIG. 12.
The ratings 175 may be received along with a first entity ID 177
that identifies the first entity for which the ratings 175 are
being provided. The weighting modification generator 194 may access
the initial weighting values 179 from the ratings database 205
using the first entity ID 177. In an embodiment, the weighting
modification generator 194 may generate the weighting modification
196 as described above in relation to Act 156 of FIG. 14.
[0249] The weighting adjuster 195 may receive the weighting
modification 196 and the initial weighting values 179, and generate
the resulting weighting values 203 as described above in relation
to Act 812. In an embodiment, the weighting adjuster 195 may
generate the resulting weighting values as described below in
relation to Acts 158 and 160 of FIG. 14.
[0250] The weighting adjuster 194 may store the resulting weighting
values 203 and the ratings database 205 for later access.
[0251] In aspects of determining recursively the impacts of two or
more ratings on a result, the initial weighting values and
resulting weighting values may be represented as weighting vectors
or weighting matrices. Further, the plurality of provided ratings
may be represented by a rating vector or rating matrix, as will be
described in more detail below. Such vectors and matrices may be
embodied tangibly on a medium using any of a variety of data
structures, for example, an array. Accordingly, the terms
"weighting array" and "rating array," as used herein, refer to a
weighting vector or matrix and a rating vector or matrix,
respectively, tangibly embodied on a medium as an array of other
data structure.
[0252] FIG. 14 is a flow chart illustrating an example embodiment
of a method of estimating the impacts of a plurality of ratings on
a result using vectors. In Act 150, a multi-rating vector is
transposed to produce a transposed multi-rating vector. Each value
of the multi-rating vector may represent an attribute or sub-rating
of an overall rating or other qualitative assessment.
[0253] Next, in Act 152, a resulting adjustment matrix may be
generated from an initial adjustment matrix, the multi-rating
vector and the transposed multi-rating vector. The resulting
adjustment matrix may be generated by applying the following
equation:
V.sub.t=F.multidot.V.sub.t-1+X.sub.tX.sub.t.sup.T, Equation 29
[0254] where V.sub.t-1 is an initial adjustment matrix, X.sub.t is
the multi-rating vector, X.sub.t.sup.T is the transposed
multi-rating vector, and V.sub.t is the resulting adjustment
matrix. As indicated in Equation 26, the initial adjustment matrix
may be weighted by a forgetting factor, F, similar to as described
above in relation to Equations 18 and 19. The forgetting factor, F,
may have the affect of giving the multi-rating vector more weight
than the initial adjustment matrix in determining the resulting
adjustment matrix. Alternatively, the forgetting factor may be
chosen such that the initial adjustment matrix has more weight in
determining the resulting adjustment matrix than the multi-rating
matrix.
[0255] In a next Act 154, the resulting adjustment matrix may be
inverted to produce an inverted adjustment matrix. In a following
Act 156, a weighting modification may be generated from the
transposed multi-rating vector, an initial weighting vector and a
result (e.g., an overall rating or other qualitative assessment).
The weighting modification may be generated by applying the
following equation:
Z.sub.mod=Y.sub.t-X.sub.t.sup.TZ.sub.t-12, Equation 30
[0256] where Y.sub.t-1 is the initial weighting vector, Y.sub.t is
the result, and Z.sub.mod is the weighting modification. The
weighting modification represents a difference between the result
Y.sub.t and an estimated result, X.sub.t.sup.TZ.sub.t-1, according
to the initial weighting vector and the multi-rating vector.
[0257] Next, in Act 158, a weighting adjustment vector may be
generated from the inverted adjustment matrix, the multi-rating
vector and the weighting modification. Such a weighting adjustment
vector may be generated by applying the following equation:
Z.sub.adj=V.sub.t.sup.-1X.sub.t.multidot.Z.sub.mod Equation 31
[0258] where V.sub.t.sup.-1 is the inverted adjustment matrix and
Z.sub.adj is the weighting adjustment vector.
[0259] The weighting adjustment, Z.sub.adj further may be weighted
as a function of any number of factors. For example, the weighting
adjustment may be weighted as a function of the reputation of the
rater that provided the multi-rating vector and overall result, or
as a function of some other factor corresponding to the transaction
on which the multi-rating vector and the overall result are based,
e.g., a factor indicative of the significance of the
transaction.
[0260] Next, in Act 160, the weighting adjustment vector may be
added to the initial weighting vector to produce a resulting
weighting vector. The resulting weighting vector may be generated
by applying the following equation:
Z.sub.t=Z.sub.t-1+Z.sub.adj, Equation 32
[0261] where Z.sub.t is the resulting weighting vector.
[0262] In an aspect of using vectors to determine weighting values,
each weighting vector includes an extra weighting value such that
if there is a number of rating parameters n, then the dimension of
the weighting vector is n+1. This extra weighting value may be used
to factor in a parameter other than a rating parameter in
determining a result parameter.
[0263] For the embodiment of FIG. 14, estimated impacts of a
plurality of ratings are determined for a single result. In such an
embodiment, weighting vectors are applied to rating vectors and
compared to a single scalar result.
[0264] Alternatively, more than one result may correspond to a
plurality of ratings. If more than one result is involved, the
plurality of results may be represented with a vector, and the
impacts of the plurality of ratings on the plurality of results may
be represented by a weighting matrix, as opposed to a weighting
vector. The use of weighting matrices and a result vector as
opposed to weighting vectors and a result scalar are intended to
fall within the scope of the invention. Weighting vectors and
result scalars are described for illustrative purposes and are not
intended to limit the scope of the invention.
[0265] Equations 29-32 are derived from the minimization of the
following recursive least squares (RLS) problem: 40 Equation33: Z
estimate = arg min S t ( Z t ) ; where S t ( Z t ) = s = 1 t B ( t
, s ) ( Y s - X s t Z t ) 2 ; B ( t , s ) = F ( t ) B ( t - 1 , s )
; and
[0266] where F(t) is the constant F described above in relation to
FIG. 14.
[0267] The above RLS problem, the theory behind the problem, and
the several matrix manipulations needed to solve equation 30 to
produce equations 26-29 are described in more detail in chapter 9
of the Madsen text.
[0268] Other statistical methods for determining recursively a
weighting vector for multiple attributes of a qualitative
assessment, for example, variations of Equation 32, may be
used.
[0269] In an aspect of determining a plurality of weighting values
indicating an impact of a plurality of ratings on a result, a
estimated result deviation may be determined. In an embodiment of
determining a estimated result deviation, the estimated result
deviation may be determined recursively. For example, the estimated
result deviation may be determined by application of the following
equation: 41 Equation34: ( RD i weight ) 2 = F .cndot. ( RD i - 1
weight ) 2 + ( Y 1 - X 1 T Z 1 ) 2 T o
[0270] where RD.sub.i.sup.weight is the resulting estimated result
deviation, 42 RD i - 1 weight
[0271] is an initial estimated result deviation, F is a forgetting
factor similar to as described above in relation to Equations 18
and 19, and T.sub.o is the effective number of estimated results
similar to as described above in relation to Equations 18 and
19.
[0272] Other statistical methods may be used to determine
reliability of estimated results based on the estimated impacts of
a plurality of ratings on a result. For example, a forgetting
factor may not be applied, or an average deviation may be
determined for all estimated deviations for the rated entity, or
such an average deviation may be calculated with each estimated
result deviation weighted according to how recent the estimated
result was determined. For example, the estimated result deviation
may be determined by applying the following equation: 43
Equation35: RD i weight = s = 1 t F t - 5 ( Y s - X 5 T Z s )
[0273] FIG. 15 is a data flow diagram illustrating an example
embodiment of the system 169 that generates a resulting weighting
vector 204 to represent the estimated weights of a plurality of
ratings in determining a result. The weighting generator 170 may
receive as input an initial adjustment matrix 174, a multi-rating
matrix 176, an overall rating (or other qualitative assessment) 178
and an initial weighting vector 180, and generate as output a
resulting weighting vector 204.
[0274] The weighting generator 170 may include an adjustment matrix
generator 182, an inverse matrix generator 190, a transposed vector
generator 186, the weighting modification generator 194 and the
weighting adjuster 195. The weighting adjuster 195 includes a
weighting adjustment generator 198 and a vector adder 202.
[0275] The transposed vector generator 186 may receive as input the
multi-rating vector 176 and generate the transposed multi-rating
vector 184 as output. The adjustment matrix generator 182 may
receive as input the transposed multi-rating vector 184 and the
initial adjustment matrix 174 and generate as output a resulting
adjustment matrix 188. The adjustment matrix generator 182 may also
receive the forgetting factor 172 as input and generate the
resulting adjustment matrix 188 based at least in part on the
forgetting factor 172. The resulting adjustment matrix 188 may be
generated by applying Equation 29.
[0276] The inverse matrix generator 190 may receive as input a
resulting adjustment matrix 188 and generate as output an inverse
adjustment matrix 192. The weighting modification generator 194 may
receive as input the transposed multi-rating vector 184 and the
overall rating 178 and generate as output a weighting modification
196. Optionally, the weighting modification generator 194 may
generate the weighting modification in accordance with Equation 30
above.
[0277] The weighting adjustment generator 198 may receive as input
the weighting modification 196, the multi-rating vector 176 and the
inverse adjustment matrix 192, and generate as output the weighting
adjustment vector 200. The weighting adjustment generator 198 may
generate the weighting adjustment vector 200 in accordance with
Equation 31 above.
[0278] The vector adder 202 may receive as input the weighting
adjustment vector 200 and the initial weighting vector 180 and
generate as output the resulting weighting vector 204. The vector
adder 202 may generate the resulting weighting vector 204 in
accordance with Equation 32.
[0279] The weighting generator 170 and any components thereof,
including adjustment matrix generator 182, inverse matrix generator
190, transposed vector generator 186, weighting modification
generator 194 weighting adjuster 195, weighting adjustment
generator 198 and vector adder 202, may be implemented as software,
hardware, firmware or any combination thereof. The weighting vector
generator 170 and any components thereof may reside on a single
machine (e.g., a computer), or may be modular and reside on
multiple interconnected (e.g., by a network) machines. Further, on
each of the one or more machines that include the weighting vector
generator 169 or a component thereof, the generator 170 or
component may reside in one or more locations on the machine. For
example, different portions of the weighting vector generator 170
or different portions of a component may reside in different areas
of memory (e.g., RAM, ROM, disk, etc.) on a computer.
[0280] The system 169, and components thereof are merely example
embodiments of a system for generating a ratee reputation. Such
example embodiments are not meant to limit the scope of the
invention and are provided merely for illustrative purposes, as any
of a variety of other systems and components for estimating the
impacts of a plurality of ratings on a result may fall within the
scope of the invention.
[0281] For any of the methods or systems described herein in
relation to FIGS. 2-17, any number of entities may be members of a
population. This population may be grouped into clusters having
common properties, for example, similar demographics or
preferences. Further, for a transaction having multiple parameters,
for example, multiple rating parameters, the entities of the
population may be grouped into any number of clusters based on the
statistical similarity of the ratings they provide for the
parameters. The entities may be grouped in to clusters using any of
a number of known clustering algorithms, for example, a k-means
clustering algorithm, an example of which is described at:
http://www.cne.gmu.edu/mo-
dules/dau/stat/clustgalgs/clust5_bdy.html.
[0282] If a population of entities is grouped into clusters,
reputations, estimators, deviations, and other values may be
determined for each cluster separately. For example, weighting
vectors and associated matrices may be generated and maintained
independently for each cluster.
[0283] VII. Generating a Third Estimated Ratee Reputation By
Combining a First Estimated Ratee Reputation With a Second
Estimated Ratee Reputation
[0284] Described above are example embodiments of systems and
methods for estimating a ratee reputation. For example, determining
an estimated ratee reputation of a first entity using the rater
reputations of one or more entities that provided ratings for the
first entity is described above in relation to FIGS. 8 and 9.
[0285] As described above, if one or more attributes are provided
for a single exchange with a ratee (e.g., one or more ratings are
provided by a rater), an estimated ratee reputation may be
determined for each attribute. As used herein, an estimated ratee
attribute reputation is an estimated ratee reputation determined
for one of multiple attributes for a single exchange, as opposed to
an estimated ratee reputation determined for a result or
qualitative assessment associated with the single exchange.
[0286] Further, determining an estimated result by applying initial
weighting values to associated attributes, e.g., received ratings,
is described above in relation to Act 808 of FIG. 12. If the
estimated result described in relation to Act 808 is an overall
rating, then initial weighting values may be applied to associated
attributes to estimate a ratee reputation of an entity.
[0287] It may be desirable to combine two or more estimated ratee
reputations to produce a new estimated ratee reputation. The
deviation of such a new estimated ratee reputation over time from
actual received ratings may be used to determine whether the new
estimated ratee reputation is more accurate than the two or more
estimated ratee reputations. Accordingly, provided herein is a
method and system for estimating a ratee reputation of a first
entity based on multiple estimated ratee reputations of the first
entity.
[0288] FIG. 16 is a flowchart illustrating an example embodiment of
a method 600 of combining a first estimated ratee reputation of a
first entity with a second estimated ratee reputation of the first
entity to produce a third estimated ratee reputation. A reputation
database, for example, the reputation database 726 described below
in relation to FIG. 17, may store a first estimated ratee
reputation and one or more ratee attribute reputations.
[0289] The first estimated ratee reputation may have been generated
by combining a plurality of overall ratings, where each overall
rating was provided by a different entity for a corresponding
transaction. For example, the first estimated ratee reputation may
be determined from the plurality of overall ratings in a similar
manner to that described above in relation to FIGS. 8 and 9.
[0290] Each of the one or more ratee attribute reputations may have
been generated by combining a plurality of attributes, where each
attribute corresponds to a different one of the transactions. For
example, each of the one or more ratee attribute reputations may be
determined from the plurality of attributes in a similar manner to
that described above in relation to FIGS. 8 and 9.
[0291] In Act 602, the one or more ratee attribute reputations are
received. In a next Act 604, for each received ratee attribute
reputation, a corresponding weighting value is accessed. The
accessed weighting value represents an estimated weight of the
received ratee attribute reputation relative to others of the one
or more received ratee attribute reputations in estimating a ratee
reputation of the first entity, similarly to as described above in
relation to Act 806 of FIG. 12.
[0292] Next, in Act 606, a second estimated ratee reputation of the
first entity is determined by applying each weighting value to its
corresponding ratee attribute reputation. For example, the
weighting values may be combined with the corresponding ratee
attribute reputations similarly to as described above in relation
to Act 808 of FIG. 12 and Act 156 of FIG. 14.
[0293] In a following Act 608, the first estimated ratee reputation
is accessed, for example, from a database. Act 608 may be performed
serially or concurrently to the sequence of Acts 602-606.
[0294] Next, in Act 610, the first estimated ratee reputation and
the second estimated ratee reputation may be combined to produce a
third estimated ratee reputation. For example, the average of the
first estimated ratee reputation and the second estimated ratee
reputation may be determined such that the third estimated ratee
reputation is an average of the first and second estimated ratee
reputations.
[0295] Optionally, the first and second estimated ratee reputations
may be weighted by estimated ratee reputation deviations to
calculate a weighted average. For example, to determine the third
estimated ratee reputation, the following equation may be applied:
44 Equation36: R 3 = RD 1 rater .cndot. R 2 + RD 2 rater .cndot. R
1 RD 1 rater + RD 2 rater ;
[0296] where R.sub.1 is the first estimated ratee reputation,
R.sub.2 is second estimated ratee reputation and R.sub.3 is the
third estimated ratee reputation. RD.sub.1.sup.rater is a first
estimated ratee reputation deviation corresponding to the first
estimated ratee reputation and may be determined by application of
Equation 21 as described above. RD.sub.2.sup.rater is a second
estimated ratee reputation deviation corresponding to the second
estimated ratee reputation and may be determined by application of
Equation 34 or 35 as described above.
[0297] As described above in relation to Equations 21, 34 and 35,
for a given estimated ratee reputation, a higher estimated ratee
reputation deviation represents a lower reliability of the
estimated ratee reputation and, conversely, a lower estimated ratee
reputation deviation represents a higher reliability of the
estimated ratee reputation. Therefore, if both the first and second
estimated ratee reputations were weighted according to their
respective ratee reputation deviations, the ratee reputation with a
higher deviation and lower reliability would be given more weight
(i.e., have a greater impact) in generating the third estimated
ratee reputation, which consequently would generate a less reliable
third estimated ratee reputation than that defined by Equation
36.
[0298] Accordingly, in Equation 36, to weight both the first and
second ratee reputations according to their respective
reliabilities, the first ratee reputation is weighted by the second
ratee reputation deviation, and the second ratee reputation is
weighted by the first ratee reputation deviation. As a result, of
the first and second ratee reputations, the ratee reputation having
the higher reliability will have the greater impact on the third
estimated ratee reputation.
[0299] FIG. 17 is a data flow diagram illustrating an example
embodiment of a system 700 for generating an estimated ratee
reputation. The system 700 may include a reputation database 726
and an estimated ratee reputation generator 706 that includes a
first ratee reputation estimator 708 and a second ratee reputation
estimator 712. The reputation database 726 may be a reputation
database or similar data structure as described below in relation
to FIG. 18.
[0300] The estimated ratee reputation generator 706 may receive
ratee attribute reputations 702 and a ratee ID 704. The ratee
attribute reputations 702 may be determined, as described above,
from attributes corresponding to transactions with a first entity,
and the ratee ID 704 may indicate the first entity. The estimated
ratee reputation generator 706 may use the ratee ID 704 to access,
from reputation database 726, weighting values 718, first estimated
ratee reputation deviation 720, second estimated ratee reputation
722 and second estimated ratee reputation deviation 724. The
estimated ratee reputation generator 706 may use values 718, 720,
722 and 724 to generate a third estimated ratee reputation 714, for
example, as described above in relation to FIG. 16.
[0301] The first ratee reputation estimator 708 may receive the one
or more ratee attribute reputations 702 and the weighting values
718, and generate the second estimated ratee reputation 710, for
example, as described above in relation to Act 606 of the FIG.
16.
[0302] The second ratee reputation estimator 712 may receive the
first estimated ratee reputation deviation 720, the second
estimated ratee reputation 722, the second estimated ratee
reputation deviation 724 and the second estimated ratee reputation
710, and generate the third estimated ratee reputation 714, for
example, as described above in relation to Act 610 of FIG. 16.
[0303] The estimated ratee reputation generator 706, and any of a
combination of its components, including first ratee reputation
estimator 708 and second ratee reputation estimator 712, may be
implemented using software, firmware or hardware or any combination
thereof. The estimated ratee reputation generator 706 and any
components thereof may reside on a single machine (e.g., a
computer), or may be modular and reside on a multiple
interconnected (e.g., by a network) machines. Further, on each of
the one or more machines that include the estimated ratee
reputation generator 706 or a component thereof, the generator 706
or component may reside in one or more locations on the machine.
For example, different portions of the estimated ratee reputation
generator 706 or different portions of a component may reside in
different areas of memory (e.g., RAM, ROM, disk, etc.) on a
computer.
[0304] System 700, and components thereof, are merely example
embodiments of a system for estimating a ratee reputation. Such
example embodiments are not meant to limit the scope of the
invention and are provided merely for illustrative purposes, as any
of a variety of other system and components for estimating a ratee
reputation may fall within the scope of the invention.
[0305] VIII. System Architecture
[0306] FIG. 18 is a data flow diagram illustrating an example
system architecture 209 for implementing the methods, systems and
variations thereof described above in relation to FIGS. 2-17. The
system 209 may include a client 210, a server 212, ratee reputation
database 234, an authentication I.D. database 236 and a rater
reputation database 238. The components 210, 212, 234, 236 and 238
of the system 209 may have a variety of configurations. For
example, all these components may reside on a single computer, or
any combination thereof may reside on a separate computer or
multiple computers interconnected, for example, by a network.
Further, any combination of these components may reside on separate
networks, including separate LANs (Local Area Networks), MANs
(Metropolitan Area Networks) and WANs (Wide Area Networks).
[0307] The ratee reputation database 234 may contain one or more
entries, where each entry represents ratee reputation data for an
entity. For example, an entry in the ratee reputation database may
include an entity I.D., demographic information about the entity,
ratings of the entity provided by other entities, a ratee
reputation of the entity, a ratee reputation deviation of the
entity, weighting values corresponding to attributes or rating
parameters or items, goods, services, people, etc. associated with
the entity, and other information regarding the entity.
[0308] The authentication I.D. database 236 may include one or more
entries, where each entry represents an entity that is part of a
population using a ratee reputation system, a rater reputation
system, or a combination thereof. An entry in the authentication
I.D. database 236 may include an I.D. of an entity, demographic
information about an entity, a password for the entity, and other
information regarding an entity.
[0309] The rater reputation database 238 may include one or more
entries, where each entry represents data specific to an entity.
For example, an entry in the rater reputation database 238 may
include an entity I.D., demographic information about the entity,
ratings provided by the entity, a rater reputation of the entity, a
rater reputation deviation of the entity, and other information
regarding the entity.
[0310] Each of the databases 234, 236 and 238 may be of any type of
a variety of types of databases, including a relational database
(e.g., Microsoft SQL or Oracle) that stores data as entries in
tables, an object-oriented database that stores data as objects or
a flat file database, where entries are stored as records and
separated by delimiters. Further, each of the databases 234, 236
and 238 may each be part of single database. For example, a single
database may maintain an entry for each entity, where each entry
includes an I.D. of the entity, authentication data of the entity,
ratings provided by the entity, ratings of the entity provided by
other entities, a ratee reputation of the entity, weighting values,
and a rater reputation of the entity, as well as other
information.
[0311] The server 212 may include a frontend 230 and a backend 232.
The frontend 230 may include a user interface and may receive and
send instructions and other data to and from the client 210. The
frontend 230 may then transfer and receive instructions to and from
the backend 232.
[0312] The backend may determine ratee reputations, rater
reputations, ratee reputation deviations, rater reputation
deviations, weightings, and other metrics and values such as, for
example, as described above in relation to FIGS. 2-17.
[0313] The backend 232 may also receive queries 216 submitted to
the frontend 230 and translate these queries to database queries on
one of the databases 234, 236 or 238. The backend 232 may also
transfer updated reputations 244, ratings 246 and weightings to the
various databases.
[0314] The client 210 and the server 212 may be implemented as
software, firmware, or hardware, or any combination thereof. For
example, the client 210 and the server 230 may be implemented as
software using any of a variety of programming languages, such as
Java, C and C++.
[0315] Through the client 210, an entity may submit one or more
ratings 224 to the server 212. Included along with the ratings 224
may be a rater ID that identifies the rating entity and a ratee ID
that identifies the entity being rated. The server 212 may then
determine a ratee reputation, a rater reputation, weightings, and
other information in accordance with the various techniques
described above in relation to FIGS. 2-17.
[0316] For example, to determine a ratee reputation of the rated
entity, the server 212 may submit a ratee query 240 to the ratee
reputation database 234 and extract various data corresponding to
the rated entity, including the current ratee reputation and
previous ratings of the rated entity provided by other entities.
This data may be sent from the ratee reputation database 234 to the
server 212 as query results 242. The server 212 then may use the
query results 242 and the received ratings 224 to determine a ratee
reputation of the rated entity according to any of a variety of the
techniques described above , then may send the ratee reputation 226
through the frontend 230 to the client 210.
[0317] The server 212 may store or persist the one or more ratings
224 and the updated ratee reputation 244 by sending to the ratee
reputation database 234, for example, to an entry corresponding to
the rated entity, and by sending the one or more ratings 224 and
the updated ratee reputation to the rater reputation database 238,
for example, to an entry corresponding to the rating entity.
[0318] Similarly, to determine a rater reputation of the rating
entity, the server 212 may submit a rater query 248 to the rater
reputation database 238 and extract various data corresponding to
the rating entity, including the current rater reputation and
previous ratings of other entities provided by the rating entity.
This data may be sent from the rater reputation database 238 to the
server 212 as query results 250. The server 212 then may use the
query results 250 and the received ratings 224 to determine a rater
reputation of the rated entity according to any of the techniques
described above through the frontend 230, the server 212, then may
send the determined rater reputation 226 to the client 210.
[0319] The server 212 may store the one or more ratings 224 and the
updated rater reputation 244 by sending to the ratee reputation
database 234, for example, to an entry corresponding to the rated
entity, and by sending the one or more ratings 224 and the updated
rater reputation 244 to the rater reputation database 238, for
example, to an entry corresponding to the rating entity.
[0320] The server 212 may use similar techniques to access the
ratee reputation database 234 and the rater reputation database 238
to determine other values as described above in relation to FIGS.
2-17, including determining ratee reputation deviations, rater
reputation deviations, personalized ratee reputations, weightings,
estimated results, and estimated result deviations.
[0321] For example, in response to receiving a plurality of ratings
224 and a result from the client 210, the server 212 may access the
necessary values in the ratee reputation database 234 and/or the
ratee reputation database 238, determine resulting weighting
values, for example, as described above in relation to FIGS. 12-15,
and then sending the weighting values 214 to the client 210.
[0322] Further, the server 212, through the frontend 230, may
receive a user query 216 from the client 210. For example, the user
query may be requesting the rater reputations of one or more
entities, the ratee reputations of one or more entities, or the
personalized ratee reputation of one or more entities from the
perspective of a particular entity. The server 212 may convert the
user query 216 into a database query, for example, a ratee query
240 or a rater query 248, and send the database query to the
appropriate database. The server 212 then may send the query
results 218 to the client 210.
[0323] The server 212 may be part of an on-line marketplace, for
example, an agent-mediated marketplace. Accordingly, the client may
request and receive marketplace information 220 from the server
230. Further, the server 212, as part of a transaction between an
entity corresponding to the client 210 and a counterpart entity,
may send communications 222 to the client 210. The communications
222 may include notifications pertaining to the current
transaction, prompts for information from the entity corresponding
to the client 210, reputations of the counterpart entity, and other
information about the entity including demographic data, weighting
values, etc.
[0324] During the context of an exchange between the entity
corresponding to the client 210 and the counterpart entity, the
client 210 may send instructions 228 to make a deal, for example,
instructions to purchase a good.
[0325] The client 210 may include a user interface to allow
interaction between a user and an application, for example, a
reputation application or marketplace application implemented using
the client 210 and server 212. The user interface may involve using
CGI scripts to generate web pages in accordance with any of a
variety of markup languages such as, for example, HTML, XML or
SGML.
[0326] The several methods described herein, and the various
embodiments thereof, including the methods and embodiments
described in relation to FIGS. 2, 3, 8, 10, 12, 14, 16 and
Equations 5-35, may be stored on a computer-readable medium as
computer-readable signals. For each method or embodiment thereof,
the computer-readable signals may define instructions that, in
response to being executed on a computer, perform the method or
embodiment. Each method or embodiment thereof may be implemented
using software, firmware, hardware, or any combination thereof. If
implemented as software, the computer-readable signals defining the
method or embodiment may be part of a computer program product,
such as a software program written in any of a number of languages
such as, for example, C, C++ or Java.
[0327] Having now described some illustrative embodiments, it
should be apparent to those skilled in the art that the foregoing
is merely illustrative and not limiting, having been presented by
way of example only. Numerous modifications and other illustrative
embodiments are within the scope of one of ordinary skill in the
art and are contemplated as falling within the scope of the
invention. In particular, although many of the examples presented
herein involve specific combinations of method Acts or apparatus
elements, it should be understood that those Acts and those
elements may be combined in other ways to accomplish the same
objectives. Acts, elements and features discussed only in
connection with one embodiment are not intended to be excluded from
a similar role in other embodiments.
* * * * *
References