U.S. patent application number 15/485111 was filed with the patent office on 2017-08-03 for endorsing local search results.
This patent application is currently assigned to Google Inc.. The applicant listed for this patent is Google Inc.. Invention is credited to Sumit Agarwal, Thomas Christopher Korte, Celia Neuhoff Saino.
Application Number | 20170220653 15/485111 |
Document ID | / |
Family ID | 35515213 |
Filed Date | 2017-08-03 |
United States Patent
Application |
20170220653 |
Kind Code |
A1 |
Korte; Thomas Christopher ;
et al. |
August 3, 2017 |
ENDORSING LOCAL SEARCH RESULTS
Abstract
Methods and systems for improving user search experience with a
search engine by providing a way for associated users to create and
share personalized lists of local search results and/or
advertisements through endorsements of such local search results
and/or ads. Local search endorsements can be used to personalize
the search engine's ranking of local search results by offering a
way for users to re-rank the results for themselves and for those
who trust them.
Inventors: |
Korte; Thomas Christopher;
(San Francisco, CA) ; Agarwal; Sumit; (Washington,
DC) ; Saino; Celia Neuhoff; (San Francisco,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Google Inc. |
Mountain View |
CA |
US |
|
|
Assignee: |
Google Inc.
Mountain View
CA
|
Family ID: |
35515213 |
Appl. No.: |
15/485111 |
Filed: |
April 11, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14308974 |
Jun 19, 2014 |
9633116 |
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15485111 |
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13618042 |
Sep 14, 2012 |
8880516 |
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14308974 |
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12912037 |
Oct 26, 2010 |
8489586 |
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13618042 |
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10879591 |
Jun 30, 2004 |
7827176 |
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12912037 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/951 20190101;
G06Q 30/0256 20130101; G06F 16/29 20190101; G06F 16/248 20190101;
G06F 16/9535 20190101; G06F 16/24578 20190101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06Q 30/02 20060101 G06Q030/02 |
Claims
1. A computer-implemented method comprising: receiving a first
search query from a first member of a member network; obtaining a
first response to the first search query, wherein the first
response identifies a plurality of electronic articles that were
each rated by at least a respective one member of the member
network; for each electronic article of the plurality of electronic
articles, obtaining a respective rating provided by each of one or
more members of the member network, for each rating, applying an
adjustment to generate an updated rating, wherein the adjustment is
based on a credibility factor associated with the member who
provided the rating or a degree of separation between the first
member and the member who provided the rating, and combining the
one or more updated ratings to generate a combined member-based
rating and using the combined member-based rating to generate a
ranking score for the electronic article; and using the ranking
scores of the plurality of electronic articles to generate a
ranking of the electronic articles; and providing, for display to
the first member, in a response to the first search query, search
results representing highest ranking electronic articles in the
ranking of the electronic articles.
2. The method of claim 1, wherein: the adjustment is based on the
credibility factor associated with the member who provided the
rating.
3. The method of claim 2, wherein: the adjustment is also based on
the degree of separation between the first member and the member
who provided the rating.
4. The method of claim 1, wherein: the adjustment is based on the
degree of separation between the first member and the member who
provided the rating.
5. The method of claim 1, wherein: the degree of separation between
the first member and the member who provided the rating is a lowest
count of eligible associations separating the first member and the
member who provided the rating, wherein an association is eligible
if a type of the association is an eligible association type.
6. The method of claim 5, wherein a friendship association type is
an eligible association type.
7. The method of claim 5, wherein a business association type is
not an eligible association type.
8. The method of claim 5, wherein an implicit association based on
geographic proximity is an eligible association type.
9. The method of claim 5, wherein an implicit association based on
having at least a threshold number of common characteristics is an
eligible association type.
10. The method of claim 1, further comprising: determining one or
more groups of eligible associations that each separate the first
member and the member who provided the rating, wherein: an
association is eligible if a type of the association is an eligible
association type, each group of eligible associations includes one
or more associations, and each association of the one or more
associations in each group has a weight; for each group of the one
or more groups of eligible associations, aggregating the weight of
each eligible association in the group to generate a cumulative
weight for the group; and determining the cumulative weight for a
group in the one or more groups of eligible associations having a
lowest cumulative weight as the degree of separation between the
first member and the member who provided the rating.
11. The method of claim 10, wherein the weight for each eligible
association is determined based on the type of the eligible
association.
12. A system comprising one or more computers and one or more
storage devices storing instructions that are operable, when
executed by the one or more computers, to cause the one or more
computers to perform operations comprising: receiving a first
search query from a first member of a member network; obtaining a
first response to the first search query, wherein the first
response identifies a plurality of electronic articles that were
each rated by at least a respective one member of the member
network; for each electronic article of the plurality of electronic
articles, obtaining a respective rating provided by each of one or
more members of the member network, for each rating, applying an
adjustment to generate an updated rating, wherein the adjustment is
based on a credibility factor associated with the member who
provided the rating or a degree of separation between the first
member and the member who provided the rating, and combining the
one or more updated ratings to generate a combined member-based
rating and using the combined member-based rating to generate a
ranking score for the electronic article; and using the ranking
scores of the plurality of electronic articles to generate a
ranking of the electronic articles; and providing, for display to
the first member, in a response to the first search query, search
results representing highest ranking electronic articles in the
ranking of the electronic articles.
13. The system of claim 12, wherein: the adjustment is based on the
credibility factor associated with the member who provided the
rating.
14. The system of claim 13, wherein: the adjustment is also based
on the degree of separation between the first member and the member
who provided the rating.
15. The system of claim 12, wherein: the adjustment is based on the
degree of separation between the first member and the member who
provided the rating.
16. The system of claim 12, wherein: the degree of separation
between the first member and the member who provided the rating is
a lowest count of eligible associations separating the first member
and the member who provided the rating, wherein an association is
eligible if a type of the association is an eligible association
type.
17. The system of claim 16, wherein an implicit association based
on geographic proximity is an eligible association type.
18. The system of claim 16, wherein an implicit association based
on having at least a threshold number of common characteristics is
an eligible association type.
19. The system of claim 12, further comprising: determining one or
more groups of eligible associations that each separate the first
member and the member who provided the rating, wherein: an
association is eligible if a type of the association is an eligible
association type, each group of eligible associations includes one
or more associations, and each association of the one or more
associations in each group has a weight; for each group of the one
or more groups of eligible associations, aggregating the weight of
each eligible association in the group to generate a cumulative
weight for the group; and determining the cumulative weight for a
group in the one or more groups of eligible associations having a
lowest cumulative weight as the degree of separation between the
first member and the member who provided the rating.
20. A computer storage medium encoded with instructions that, when
executed by one or more computers, cause the one or more computers
to perform operations comprising: receiving a first search query
from a first member of a member network; obtaining a first response
to the first search query, wherein the first response identifies a
plurality of electronic articles that were each rated by at least a
respective one member of the member network; for each electronic
article of the plurality of electronic articles, obtaining a
respective rating provided by each of one or more members of the
member network, for each rating, applying an adjustment to generate
an updated rating, wherein the adjustment is based on a credibility
factor associated with the member who provided the rating or a
degree of separation between the first member and the member who
provided the rating, and combining the one or more updated ratings
to generate a combined member-based rating and using the combined
member-based rating to generate a ranking score for the electronic
article; and using the ranking scores of the plurality of
electronic articles to generate a ranking of the electronic
articles; and providing, for display to the first member, in a
response to the first search query, search results representing
highest ranking electronic articles in the ranking of the
electronic articles.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 14/308,974, filed Jun. 19, 2014, which is a continuation of
U.S. application Ser. No. 13/618,042, filed Sep. 14, 2012, which is
a continuation of U.S. application Ser. No. 12/912,037, filed Oct.
26, 2010, which is a continuation of U.S. application Ser. No.
10/879,591, filed Jun. 30, 2004, which is related to U.S.
application Ser. No. 10/879,592, filed Jun. 30, 2004. The contents
of each are hereby incorporated by reference.
BACKGROUND OF THE INVENTION
[0002] Field of the Invention
[0003] The present invention relates generally to methods and
systems for searching. For example, embodiments of the present
invention relate generally to methods and systems for using member
networks to improve a user's search experience with a search
engine.
[0004] Background
[0005] A conventional search engine, such as the Google.TM. search
engine, returns a result set in response to a search query
submitted by a user. The search engine performs the search based on
a conventional search method. For example, one known method,
described in an article entitled "The Anatomy of a Large-Scale
Hypertextual Search Engine," by Sergey Brin and Lawrence Page,
assigns a degree of importance to a document, such as a web page,
based on the link structure of the web page. The search engine
ranks or sorts the individual articles or documents in the result
set based on a variety of measures. For example, the search engine
may rank the results based on a popularity score. The search engine
generally places the most popular results at the beginning of the
result set. Some conventional search engines also include
electronic yellow pages to provide searches of individual
product/service providers (e.g., restaurants, tax services, auto
repair services, etc.) in a particular locality. Such local
searches enable users to locate desired product/service providers
that do not ordinarily appear in regular searches because they do
not have their own websites or URLs.
[0006] Conventional websites (also written as "Web sites") such as
those hosted on Yahoo!.TM. Tribe.TM., Tickle.TM., or other web
sites, allow users to form communities, groups, and/or other member
networks. The member networks on conventional websites allow
members of the group to communicate with each other and list
announcements associated with the community. Generally,
conventional web sites do not connect the member networks with
search engines and enable members of such networks to endorse or
recommend search results, particularly online advertisements and/or
search results of local individual product/service providers, to
one another.
SUMMARY OF THE INVENTION
[0007] Embodiments of the present invention provide methods and
systems for members of a member networks to endorse or recommend to
other members or users local articles and/or advertisements
(hereinafter, "ads") for particular search queries. In one
embodiment of the present invention, there is provided a method
comprising: receiving endorsement information of endorsed local
articles or ads; receiving a local search query; and providing a
search result set relevant to the local search query, wherein the
search result set includes at least one endorsed article identifier
for one of the endorsed local articles or ads.
[0008] In another embodiment of the present invention, there is
provided a method comprising: receiving a first user profile in a
member network created by a first user; receiving a first local
search query submitted by the first user; returning from a search
engine to the first user a local search result set relevant to the
first local search query, the local search result set includes one
or more links for endorsing local articles identified in the search
result set; receiving from the first user a first endorsement for
one of the local articles identified in the local search result
set; storing the first endorsement for the local article in a
member network database; receiving a second user profile in the
member network created by a second user; receiving a second local
search query submitted by the second user that is substantially
identical or relevant to the first local search query; returning
from the search engine a second local search result set relevant to
the second local search query; returning from the member network
database a third local search result set relevant to the second
search query; and merging the second local search result set with
the third local search result set to provide the second user with a
final local search result set identifying the first endorsement for
the second local search query.
[0009] The aforementioned embodiments are mentioned not to limit or
define the invention, but to provide an example of embodiments of
the invention to aid understanding thereof. Such exemplary
embodiments are discussed in the Detailed Description, and further
description of the invention is provided there. Advantages offered
by the various embodiments of the present invention may be further
understood by examining this specification.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The embodiments of the present invention are illustrated by
way of example in, and not limited to, the following figures:
[0011] FIG. 1 is a block diagram illustrating an exemplary
environment in which embodiments of the present invention may
operate;
[0012] FIG. 2 depicts a diagram of a member network in accordance
with an embodiment of the present invention; and
[0013] FIGS. 3A-B depict process flows for local search
endorsements in accordance with an embodiment of the present
invention.
[0014] FIG. 4 depicts a sample screenshot of a local articles page
in accordance with an embodiment of the present invention;
[0015] FIG. 5 depicts a sample screenshot of an endorsement page in
accordance with an embodiment of the present invention; and
[0016] FIGS. 6A-B depict process flows for local search
endorsements in accordance with another embodiment of the present
invention.
DETAILED DESCRIPTION OF THE INVENTION
Overview
[0017] Embodiments of the present invention provide methods and
systems for members of a member networks to endorse or recommend to
other members or users a localized article or articles, which may
include individual product/service providers and/or ads for a
desired locality. In one embodiment, the method begins with a user
signing up to become a member of a member network. After becoming a
member, the user can endorse or recommend a local article(s) or
ad(s). When an endorsed article identifier appears in a result set
relevant to a local search query, the article identifier can have
associated endorsement data. Further, as a member, the user can
submit local user queries to the search engine and receive lists of
search results that include article identifiers of endorsed
articles and/or ads from other members of the member network. Each
list of article identifiers in a local search result set can be
re-ordered or re-ranked to reflect those endorsed article
identifiers within the list. Thus, the search endorsements can be
used to improve the search engine's ranking of local search results
and endorsed ads by offering a way for users to re-rank the local
search results and endorsed ads for themselves and for those who
trust them.
System Architecture
[0018] Various systems in accordance with the present invention may
be constructed. FIG. 1 is a block diagram illustrating an exemplary
system in which embodiments of the present invention can operate.
The present invention may operate, and be embodied, in other
systems as well.
[0019] Referring now to the drawings in which like numerals
indicate like elements throughout the several figures, FIG. 1 is a
block diagram illustrating an exemplary system in accordance with
an exemplary embodiment of the present invention. The system 100
shown in FIG. 1 includes multiple client devices 102a-n with users
112a-112n in communication with a search site 150 and a member
network site 160 over a network 106. The search site 150 and the
member network site 160 are also in communication with each other
directly (as shown by the dashed line) or through the network 106.
The network 106 can be a wired or wireless network. Further, it can
be a public network, e.g., the Internet, or a private data network,
e.g., a local area network (LAN) or a wide area network (WAN).
Moreover, methods according to the present invention may operate
within a single computer.
[0020] Each of the client devices 102a-n includes a memory 108,
which can be a computer-readable medium (CRM), such as a random
access memory (RAM), coupled to a processor 110. The processor 110
executes computer-executable program instructions stored in the
client device, such as memory 108, as program code. Such processor
may include a microprocessor, an ASIC, and state machines. Such
processors include, or may be in communication with, media, for
example computer-readable media, which stores instructions that,
when executed by the processor, cause the processor to perform the
methods described herein. Moreover, the processor 110 can be any of
a number of computer processors, such as processors from Intel
Corporation of Santa Clara, Calif. and Motorola Corporation of
Schaumburg, Ill. Embodiments of computer-readable media include,
but are not limited to, an electronic, optical, magnetic, or other
storage or transmission device capable of providing a processor,
such as the processor 110 of client 102 a, with computer-readable
instructions. Other examples of suitable media include, but are not
limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip,
ROM, RAM, an ASIC, a configured processor, all optical media, all
magnetic tape or other magnetic media, or any other medium from
which a computer processor can read instructions. Also, various
other forms of computer-readable media may transmit or carry
instructions to a computer, including a router, switch, private or
public network, or other transmission device or channel, both wired
and wireless. The instructions may include code from any suitable
computer-programming language, including, for example, C, C++, C#,
Visual Basic, Java, Python, Perl, and JavaScript.
[0021] Client devices 102a-n can also include a number of external
or internal devices such as a mouse, a CD-ROM drive, a DVD drive, a
keyboard, a display, or other input or output devices. Examples of
client devices 102a-n are personal computers, digital assistants,
personal digital assistants (PDAs), cellular phones, mobile phones,
smart phones, pagers, digital tablets, laptop computers, Internet
appliances, and other processor-based devices. In general, the
client devices 102a-n can be any type of processor-based platform
that operates on any operating system capable of supporting one or
more client application programs. Client devices 102a-n may operate
on any operating system capable of supporting a browser or
browser-enabled application, such as Microsoft.RTM. Windows.RTM. or
Linux. The client devices 102a-n shown include, for example,
personal computers executing a browser application program such as
Microsoft Corporation's Internet Explorer.TM., Netscape
Communication Corporation's Netscape Navigator.TM., and Apple
Computer, Inc.'s Safari.TM..
[0022] Through the client devices 102a-n, users 112a-n can
communicate over the network 106 with each other and with other
sites, systems and devices coupled to the network 106. As shown in
FIG. 1, a search site 150 and a member network site 160 are also
coupled to the network 106.
[0023] The search site 150 shown includes a server device 152
executing a search application program, also known as a member
network engine 168. The member network engine 168 allows users,
such as user 112a, to interact with and participate in a member
network. A member network can refer to a computer network
connecting entities, such as people or organizations, by a set of
social relationships, such as friendship, co-working, or
information exchange. Of course, a member network can refer to a
computer application or data connecting such entities by such
social relationships. Examples of member networks include Orkut.com
and Friendster.com.
[0024] Member networks can comprise any of a variety of suitable
arrangements. An entity or member of a member network can have a
profile and that profile can represent the member in the member
network. The member network can facilitate interaction between
member profiles and allow associations or relationships between
member profiles. Associations between member profiles can he one or
more of a variety of types, such as friend, co-worker, family
member, business associate, common-interest association, and
common-geography association. Associations can also include
intermediary relationships, such as friend of a friend, and degree
of separation relationships, such as three degrees away.
[0025] Associations between member profiles can be reciprocal
associations. For example, a first member can invite another member
to become associated with the first member and the other member can
accept or reject the invitation. A member can also categorize or
weigh the association with other member profiles, such as, for
example, by assigning a level to the association. For example, for
a friendship-type association, the member can assign a level, such
as acquaintance, friend, good friend, and best friend, to the
associations between the member's profile and other member
profiles. In one embodiment, the member network engine 168 can
determine the type of association between member profiles,
including, in some embodiments, the degree of separation of the
association and the corresponding weight or level of the
association.
[0026] Similar to the client devices 102a-n, the server device 152
shown includes a processor 154 coupled to a CRM 156. Server device
152, depicted as a single computer system, may he implemented as a
network of computer processors. Examples of the server device 162
are servers, mainframe computers, networked computers, a
processor-based device, and similar types of systems and devices.
The server processor 154 can be any of a number of computer
processors, such as processors from Intel Corporation of Santa
Clara, Calif. and Motorola Corporation of Schaumburg, Ill.
[0027] Memory 156 contains a search application program, also known
as a search engine 158. The search engine 158 locates relevant
information in response to a search query from one of the client
devices 102a-n, e.g., the client device 102a. In the embodiment
shown, the server device 152, or related devices, has previously
performed a crawl of the network 106 to locate articles, such as
web pages, stored at other devices or systems coupled to the
network 106, and indexed the articles in an article index for
storage in memory 156 or another data storage device. Thus, the
search engine 158 can locate relevant information by accessing the
article index in response to a search query. The search engine 158
then provides a result set to the client device 102a via the
network 106. The result set comprises one or more identifiers of
articles that are relevant to the search query. Articles include,
for example: word processor, spreadsheet, presentation, e-mail,
instant messenger, database, and other client application program
content files or groups of files; web pages of various formats
(e.g., HTML, XML, XHTML); portable document format (PDF) files;
audio tiles; video files; or any other documents or groups of
documents or information of any type whatsoever. An article
identifier may be, for example, a uniform resource locator (URL), a
uniform resource identifier (URI), a file name, a link, an icon, a
path for a local file, or anything else that identifies an article
or ad.
[0028] The member network site 160 shown includes a member network
database 170 and a server device 162 executing a member network
engine application program. Similar to the client devices 102a-n,
the server device 162 shown includes a processor 164 coupled to a
CRM 166. The server device 162 is in communication with a member
network database 170. Server device 162, depicted as a single
computer system, may be implemented as a network of computer
processors. Examples of the server device 162 are servers,
mainframe computers, networked computers, a processor-based device,
and similar types of systems and devices. The server processor 164
can be any of a number of suitable computer processors, such as
processors from Intel Corporation of Santa Clara, Calif. and
Motorola Corporation of Schaumburg, Ill.
[0029] Memory 166 in the server device 162 contains a member
network engine application program, also known as a member network
engine 168. The member network engine 168 allows users, such as
user 112a, to interact with and participate in a member network. A
member network can refer to a computer network connecting people or
organization by a set of relationships, such as social
relationships like friendship, co-working, or information exchange.
A member network can include profiles that can be associated with
other profiles. Each profile may represent a member and a member
can be, for example, a person, an organization, a business, a
corporation, a community, a fictitious person, or other entity.
Each profile can contain entries, and each entry can include
information associated with a profile. Examples of entries for a
person profile can include information regarding relationship
status, birth date, age, children, ethnicity, religion, political
view, sense of humor, sexual orientation, fashion preferences,
smoking habits, drinking habits, pets, hometown location, passions,
sports, activities, favorite books, music, TV, or movie
preferences, favorite cuisines, email addresses, location
information, IM name, phone number, address, skills, career, or any
other information describing, identifying, or otherwise associated
with a profile. Entries for a business profile can include market
sector, customer base, location, supplier information, net profits,
net worth, number of employees, stock performance, or other types
of information associated with the business profile.
[0030] Additionally, entries within a profile can include
associations with other profiles. Associations between profiles
within a member network can include, for example, friendships,
business relationships, acquaintances, community or group
associations, activity partner associations, common interest
associations, common characteristic associations, or any other
suitable type of relationship connection (e.g., social relationship
connection). Associations between profiles can also have various
levels. For example, friendship levels can include, for example, a
"haven't met" level, an "acquaintance" level, a "friend" level, a
"good friend" level, a "best friend" level, and other suitable
levels.
[0031] A degree of separation based on associations between
profiles can also be determined. For example, a degree of
separation can be determined based on the fewest number of
associations between two profiles. Thus, if profile A is a friend
of profile B, and profile B is a friend of profile C, there can be
a degree of separation of two between profiles A and C. A degree of
separation can be type specific or type neutral. Type specific
degrees of separation only count relationships of a certain type.
Thus, for example, in the case above where A is a friend of B, and
B is a friend of C, there is a friendship degree separation of two,
even if A is directly associated with C by a business association,
which would otherwise produce a degree of separation of 1.
[0032] Moreover, each profile can also contain local-search
endorsement entries, each entry can include information associated
with an endorsed local article. For example, a local-search
endorsement entry can include a particular local search query, one
or more article identifiers for local articles and/or ads that the
user has endorsed for the local search query, and the kind of
endorsement for each of the endorsed local endorsed articles and/or
ads.
[0033] Server device 162 of the member network site 160 also
provides access to storage elements, such as a member network
storage element, in the example shown in FIG. 1, a member network
database 170. The member network database 170 can be used to store
profiles of members in a member network and to store communities
within the member network as created by the member-network engine
168. Data storage elements may include any one or combination of
methods for storing data, including without limitation, arrays,
hash tables, lists, and pairs. Other similar types of data storage
devices can be accessed by the server device 162. The member
network engine 168 can receive data comprising the profiles and
communities from the member-network database 170 and can also send
data comprising communities and profiles to the member network
database 170 for storage. The member-network database 170 may be
physically attached or otherwise in communication with the
member-network engine 168 by way of a network or other
connection.
[0034] In operation, upon receiving a search query from a user,
such as one of the users 112a-n, the search engine 158 locates
relevant information in response to the search query. The search
engine 158 then returns a first result set of one or more article
identifiers relevant to the search query. The search engine 158
also communicates with the member network engine 168 to access the
member network database 170, look up local-search endorsement
entries in member profiles that are associated with the user in a
member network as further explained later, and return a second
result set of one or more endorsed local article identifiers. The
two search result sets are then merged to provide the user with a
final search result set having article identifiers for local
articles and/or ads that are relevant to the search queries, with
some article identifiers indicated as having been endorsed based on
the second search result set. The final search result set also
provides the user with an option to endorse one or more local
articles and/or ads in the member profile. The methods for
accomplishing these tasks are described below in the process
section.
[0035] It should be noted that the present invention may include
systems having different architecture than that which is shown in
FIG. 1. For example, in some systems according to the present
invention, server device 162 may include a single physical or
logical server. The system 100 shown in FIG. 1 is merely exemplary,
and is used to help explain the member networks and methods
illustrated in subsequent figures.
Exemplary Member Network
[0036] FIG. 2 shows a diagram of a member network 200 according to
one embodiment of the present invention. According to the
embodiment illustrated in FIG. 2, the member network 200 is
illustrated with a graph comprising vertices 202, 204, 206, 208,
210, 212, and 214 and edges 218, 220, 222, 224, 226, 228, 230, 232,
and 234. The vertices 202, 204, 206, 208, 210, 212, and 214
comprise profiles A, B, C, D, E, F, and G, respectively. Each
profile can represent a member profile of a member of the member
network 200. The exemplary network 200 shown in FIG. 2 has seven
members. Considerably more members can be part of the member
network 200. A member can be an entity such as, for example, a
person, an organization, a business, a corporation, a community, a
fictitious person, or other suitable entity.
[0037] Each member profile can contain entries, and each entry can
comprise information associated with a profile. For example, a
person's member profile can contain: personal information, such as
relationship status, birth date, age, children, ethnicity,
religion, political view, sense of humor, sexual orientation,
fashion preferences, smoking habits, drinking habits, pets,
hometown location, passions, sports, activities, favorite books or
music, TV or movie preferences, and favorite cuisines; contact
information, such as email addresses, location information, instant
messenger name, telephone numbers, and address; professional
information, such as job title, employer, and skills; educational
information, such as schools attended and degrees obtained, and any
other suitable information describing, identifying, or otherwise
associated with a person. A business' member profile can, for
example, contain a description of the business, and information
about its market sector, customer base, location, suppliers, net
profits, net worth, number of employees, stock performance, contact
information, and other types of suitable information associated
with the business.
[0038] A member profile can also contain rating information
associated with the member. For example, the member can be rated or
scored by other members of the member network 200 in specific
categories, such as humor, intelligence, fashion, trustworthiness,
sexiness, and coolness. A member's category ratings can be
contained in the member's profile. In one embodiment of the member
network, a member can have fans. Fans can be other members who have
indicated that they are "fans" of the member. Rating information
can also include the number of fans of a member and identifiers of
the fans. Rating information can also include the rate at which a
member accumulated ratings or fans and how recently the member has
been rated or acquired fans.
[0039] A member profile can also contain membership information
associated with the member. Membership information can include
information about a member's login patterns to the member network,
such as the frequency that the member logs in to the member network
and the member's most recent login to the member network.
Membership information can also include information about the rate
and frequency that a member profile gains associations to other
member profiles. in a member network that comprises advertising or
sponsorship, a member profile may contain consumer information.
Consumer information may include the frequency, patterns, types, or
number of purchases the member makes, or information about which
advertisers or sponsors the member has accessed, patronized, or
used.
[0040] A member profile may comprise data stored in memory. The
profile, in addition to comprising data about the member, can also
comprise data relating to others. For example, a member profile can
contain an identification of associations or virtual links with
other member profiles. In one embodiment, a member's member network
member profile may comprise a hyperlink associated with another
member's profile. In one such association, the other member's
profile may contain a reciprocal hyperlink associated with the
first member's profile. A member's profile may also contain
information excerpted from another associated member's profile,
such as a thumbnail image of the associated member, his or her age,
marital status, and location, as well as an indication of the
number of members with which the associated member is associated.
In one embodiment, a member's profile may comprise a list of other
member network members' profiles with which the member wishes to be
associated.
[0041] An association may be designated manually or automatically.
For example, a member may designate associated members manually by
selecting other profiles and indicating an association that can be
recorded in the member's profile. Also, an association between two
profiles may comprise an association automatically generated in
response to a predetermined number of common entries, aspects, or
elements in the two members' profiles. in one embodiment, a member
profile may be associated with all of the other member profiles
comprising a predetermined number or percentage of common entries,
such as interests, hobbies, likes, dislikes, employers and/or
habits.
[0042] Associations between profiles within a member network can be
of a single type or can be multiple types and can include, for
example, friendship associations, business associations, family
associations, community associations, school associations, or any
other suitable type of link between profiles. Associations can
further be weighted to represent the strength of the association.
For example, a friendship association can be weighted more than a
school association. Each type of association can have various
levels with different weights associated with each level. For
example, a friendship association can be classified according to
which of a plurality of friendship association levels it belongs
to. In one embodiment, a friendship association may be assigned a
level by the member from a list of levels comprising: a best
friend, a good friend, a regular friend, an acquaintance, and a
friend the member has not met.
[0043] In FIG. 2, the edges 218, 220, 222, 224, 226, 228, 230, 232,
and 234 shown comprise associations between profiles. According to
the embodiment shown in FIG. 2, the member network 200 comprises a
plurality of differing types of associations represented by edges
218, 220, 222, 224, 226, 228, 230, 232, and 234. The types of
associations shown in FIG. 2 for illustration purposes are business
associations, activity partner associations, friendship
associations, community associations, and common characteristic
associations. Common characteristic associations may include, for
example, associations based on some characteristic, such as
attending the same high school or being from the same hometown, and
can indicate a lower level of significance than another type of
association, such as a friendship association.
[0044] Referring to FIG. 2, edge 220 and edge 222 each comprise an
association between profile A at vertex 202 and profile D at vertex
208. The edge 220 represents a business association, and the edge
222 represents a friendship association. Profile A is also
associated with profile E by a common characteristic association
comprising edge 218. The association between profile A and profile
E may be more attenuated than the association between profile A and
D, but the association can still be represented by the member
network depicted in FIG. 2.
[0045] Each member represented by the profiles A, B, C, D, E, F,
and G comprising the vertices 202, 204, 206, 208, 210, 212, and
214, respectively, for purposes of illustration, comprises a
person. Other types of members can be in member network 200. For
example, communities, special interest groups, organizations,
political parties, universities, and legal persons, such as
corporations and business partnerships may be members of the member
network 200. The associations 218, 220, 222, 224, 226, 228, 230,
232, and 234 illustrated in FIG. 2 comprise bi-directional
associations. An association between two profiles may comprise a
bi-directional association when both parties to the association are
associated with each other. For example, in FIG. 2, profile A is
associated with profile D, and profile D is also associated with
profile A. In one embodiment, profiles A and D will not be
bi-directionally associated with each other until both profiles
consent to such an association. For example, profile A may invite
profile D to be associated therewith, and the bi-directional
association occurs upon profile D's acceptance of such invitation.
The invitation, for example, may include sending an email or other
message to profile D indicating that profile A has requested an
association with profile D.
[0046] Other embodiments of the present invention may comprise
directed associations or other types of associations. Directed
associations can associate a first profile with a second profile
while not requiring the second profile to be associated with the
first profile. For example, profile A can be associated by a
friendship association with profile B, and profile B can be
unassociated with profile A, or profile B can be associated with
profile A through a different type of association, such as a
business association. Thus a display of profile A's friends would
include profile B, but a display of profile B's friends would not
include profile A.
[0047] Within a member network, a degree of separation can be
determined for associated profiles. In one embodiment, a degree of
separation between two profiles can be determined by the fewest
number of edges of a certain type separating the associated
profiles. In another embodiment, a type-specific degree of
separation may be determined. A type-specific degree of separation
comprises a degree of separation determined based on one particular
type of association. For example, a profile A has a friend
association degree of separation of two from profile E. The fewest
number of friendship associations between profile A and profile E
is two--the friendship association comprising edge 222 between
profiles A and D and the friendship association comprising edge 234
between profiles D and E. Thus, for the associated profiles A and
E, the degree of friendship separation, determined according to one
aspect of one embodiment of the present invention, is two.
[0048] Another type-specific degree of separation can be determined
for profiles A and E. For example, a common characteristic degree
of separation can be determined by determining the fewest number of
common characteristic associations separating profile A and profile
E. According to the embodiment depicted in FIG. 2, there is one
common characteristic association, comprising edge 218, separating
profiles A and E. Thus, the co on characteristic association degree
of separation, according to the embodiment depicted in FIG. 2, is
one. The common characteristic in this example, can be that profile
A attended the same high school as profile E. A common
characteristic association may be selected by profiles A and E to
represent that they are associated in some fashion, but to not
create a close association such as with a friendship
association.
[0049] According to other aspects of certain embodiments of the
present invention, the degree of separation may be determined by
use of a weighting factor assigned to each association. For
example, close friendships can be weighted higher than more distant
friendships. According to certain aspects of embodiments using a
weighting factor, a higher weighting factor for an association can
reduce the degree of separation between profiles and lower
weighting factors can increase the degree of separation. This can
be accomplished, for example, by establishing an inverse
relationship between each associations and a corresponding
weighting factor prior to summing the associations. Thus, highly
weighted associations would contribute less to the resulting sum
that lower weighted associations. Process
[0050] Various methods or processes in accordance with the present
invention may be constructed. For example, in one embodiment, the
method begins with receiving profiles of users in a member network,
wherein the profiles comprise endorsement information of local
articles and/or ads. The endorsement information can include a
look-up table listing a category classifying local search queries,
an article identifier for an endorsed local article or ad in the
category, and an endorsement associated with the article
identifier. The endorsements come from members of the member
network, and they can be binary endorsements of the local articles
and/or ads, ratings of the local articles and/or ads, and/or
comments about the local articles and/or ads. Financial incentives
can be provided to endorsing users. The method also includes
receiving a local search query from a user within or outside of the
member network and providing local articles relevant to the local
search query, wherein one of the local articles may be endorsed
based on the endorsement information. When the received local
search query is classified in the same category found in the
endorsement information, the provided local article for the
received local search query is the endorsed local article listed in
the endorsement information. The endorsed local articles and/or ads
are from members associated with the user that submits the received
local search query. The associations can be based on profile
associations indicated in user profiles and/or the received local
search query.
[0051] FIGS. 3A-B illustrate an exemplary method that provides
local search endorsements whereby members can create and share
their personalized lists of local articles and/or ads with other
members in the member network and/or other users of a search
engine. The exemplary method is provided by way of example, as
there are a variety of ways to carry out methods according to the
present invention. The method shown in FIGS. 3A-B can be executed
or otherwise performed by one or a combination of various systems.
The method in FIGS. 3A-B is described below as carried out by the
system 100 shown in FIG. 1 by way of example, and various elements
of the system 100 are referenced in explaining the example method
of FIGS. 3A-B.
[0052] The method begins at 310 with a user, such as user 112a,
joining a member network, such as the member network 200 (e.g.,
Orkut.TM.), by creating a user or member profile as described
earlier.
[0053] At 320, the user 112a submits a local search query to a
search engine, such as the search engine 158 at the search site
152, that is linked to the member network site 160. The local
search query includes one or more items to be searched (e.g., a
sushi restaurant) and a particular locality for the search (e.g.,
San Francisco). The user 112a submits the local search query by
generating a query signal that includes the local search query at
the client device 102a and transmitting the query signal to the
server device 152 via the network 106.
[0054] At 330, in response to receiving the query signal from the
client device 102a, the search engine 158 locates the desired local
information in a manner know in the art and return article
identifiers representing the desired local information in a local
search result set. The search engine 158 further categorizes the
local search query. For instance, referring to the example of the
local search query for a sushi restaurant in San Francisco, such
query can be categorized under "Japanese or Sushi Restaurants", and
listed under a locality subcategory of "San Francisco".
Alternatively, such query can be categorized under the San
Francisco locality and listed under a subcategory for "Japanese or
Sushi Restaurants". Thus, the types of categorization used for
local search queries merely depend on the desire of the search site
150.
[0055] Each article identifier in the local search result set
returned by the search engine 158 is provided with one or more
"endorse" links that enables the user 112a to endorse or recommend
the underlying local article represented by the article identifier
for the particular issued search query. The
endorsement/recommendation can be a simple binary endorsement
(e.g., a positive or negative endorsement) of the local article, a
scaling system (e.g., 1 to 5 or A to F to indicate best to worst)
rating the local article, and/or other added comments about the
local article.
[0056] At 340, the user 112a can endorse a local article by
accessing the respective "endorse" link(s). For instance, the user
112a can click on the "endorse" link and be directed to a
navigational page or window, wherein the user 112a is presented
with the option to either positively endorse (e.g., "Recommended")
or negatively endorse (e.g., "Not Recommended") the local article.
In another instance, the user 112a can click on the "endorse" link
and be directed to a navigational page or window, wherein the user
112a is presented with a scale, e.g., from 1 to 5 to rate the local
article from best to worse or vice versa. In still another
instance, separately or in combination with the aforementioned two
instances, the user 112a can click on the "endorse" link and be
directed to a navigational page or window, wherein the user 112a
has the option to add comments about his/her opinion of the local
article.
[0057] Alternatively, the user 112a can endorse a local article
without having to first obtain the local article from a search
query. For instance, the user 112a can create a search endorsement
entry in his or her member profile, wherein the user 112a can input
a desired search query for the endorsement, one or more article
identifiers of local articles that the user 112a chooses to endorse
for the desired search query, and the kind of endorsement for each
of the endorsed local article identifiers.
[0058] In a further instance, the user 112a does not click on the
available "endorse" link but click on the actual local article
identifier to navigate to the actual local article. The search
engine 158 then notes such access and transmits that information to
the member network site 160 for storage in the profile of the user
112a in the member network database 170. At some point in the
future, when the user 112a accesses the search engine 158 for
another search, and the search engine 158, communicating with the
member network engine 168, links the user 112a with his/her member
profile in the member network database 170, the search engine 158
can then prompt the user 112a to endorse the particular local
article that the user 112a previously accessed but did not endorse.
The user 112a can receive such prompt for endorsement the next time
he/she accesses the search engine 158 or after a predetermined
number of accesses to the search engine 158. Also, the user 112a
may be prompted one or more times as desired. If the user 112a
chooses to disregard the prompts, the search engine 158 can stop
prompting the user 112a to endorse such local article after a
predetermined number of prompts. Subsequently, the search engine
158 can bring up another article identifier for a local article
that the user 112a previously accessed but did not endorse and
repeat the above prompt process again.
[0059] At 345, the user 112a can also endorse any number of ads for
a particular local search query in addition to or in lieu of the
local articles. As referred herein, an ad can be an online ad, such
as a banner ad, a pop-up window ad, or simply a listing of a name
of a product/service provider along with contact information. An ad
can also be in any format presently known or prospectively
contemplated in the art and accessible via its article identifier
by the search engine 158 and member network engine 168. According
to one embodiment of the present invention, the act of endorsing an
ad is separate from the act of accessing the search engine 158 to
perform a search as described above. Also, the endorsed ad may or
may not contain the same information as one of the local articles
that the search engine 158 can come up with for a particular local
search query. For instance, prior or subsequent to accessing the
search engine 158 for a search, the user 112a may have endorsed an
ad through a website that handles such ad. The ad website can then
forwards information about the endorsed ad to the member network
site 160 so that the member network engine 168 can store the
endorsed ad along with the proper local category for the ad in the
profile of the user 112a in the member network database 170. The
categorization of endorsed ads is similar to that for local
articles as described earlier.
[0060] At 350, once the user 112a has endorsed one or more local
articles as represented by their article identifiers in the local
search result set and/or the user 112a has endorsed one or more ads
as also represented by their article identifiers, the search engine
158 can communicate with the member network engine 168 to store the
endorsements in the member profile in the member network database
170. The endorsements are categorically stored in accordance with
the category in which the local search query submitted by the user
112a is listed.
[0061] Accordingly, the member profile of the user 112a can include
at least the following information: 1) an identifier of the user
112a (who can be anonymous); 2) a list of other users in the member
network 200 that have one or more associations with the user 112a
as described earlier; 3) a list of categories of local search
queries that have local search endorsements; 4) a list of endorsed
local articles (e.g., names of a product/service providers and
their contact information) and/or ads in each of the categories
previously submitted by the user 112a; and/or 5) the endorsements
by the user 112a for each of the endorsed local articles and/or
ads. Alternatively, the endorsements can include the aforementioned
information but be stored in a file other than the member profile
and yet be associated with the member.
[0062] Referring now to FIG. 3B, at 360, another user, such as user
112b, submits a local search query to the same search site 150. As
described earlier with regard to 320, the user 112b submits the
local search query at 370 by generating a query signal that
includes a local search query at the client device 102b and
transmitting the query signal to the server device 152 via the
network 106.
[0063] In response to receiving the query signal from the client
device 102b at 370, the search engine 158 performs two searches: 1)
at 371, a regular local search similar to 330 in FIG. 3A whereby
the search engine 158 locates the desired local information in a
manner know in the art and return article identifiers associated
with the local information in a first local search result set; and
2) at 372, a search of the member network database 170 whereby the
search engine 158 locates previously endorsed local articles and/or
ads in the same category or categories as the local search query
submitted by the user 112b in associated member profiles in a
member network and return them in a second local search result set.
The search engine 158 searches the member network database 170 by
first communicating with the server device 162 and its member
network engine 168 to identify members in the member network 200
that are associated with the user 112b and/or associated with the
local search query submitted by the user 112b. Next, through the
member network engine 168, the search engine 158 can access the
member profiles of those associated members to look up any
available list of endorsed local articles and/or ads (via their
article identifiers) in the appropriate one or more categories as
described.
[0064] Thus, the user 112b can add a layer of trust on top of the
regular local search result set. The trust can be in the form of
local search endorsements from those members of the member network
200 that are associated with the user 112b because of their
explicit profile associations with the user 112b, as described
earlier with reference to FIG. 3. The trust can also be in the form
of local search endorsements from those members that are associated
with the user 112b because of their implicit profile associations
with the user 112b and/or the local search query submitted by the
user 112b. In one implicit profile association, the search engine
158 and member network engine 168 can identify from member profiles
stored in the member network database 170 those members that have
the same or similar interests with the user 112b, based on a
comparison between the profile of the user 112b and profiles of
other users in the member network 200 or a comparison between the
profiles of other users in the member network 200 and the local
search query submitted by the user 112b. The search engine 158 and
member network engine 168 can then provide the user 112b with any
local article and/or ad endorsements that are classified in the
same category or categories with the local search query submitted
by the user 112b from such implicitly-associated members. For
instance, if the user 112b is searching for a sushi restaurant in
San Francisco, the user 112b can receive endorsements from those
members that live in the San Francisco area and like Japanese or
sushi restaurant or from those members that are food critics in the
San Francisco area. To provide another example, if both the users
112a and 112b are members of a "photography" community within the
member network 200, and the user 112b is searching for a digital
camera, the user 112b can receive any endorsements from the user
112a and other members of the "photography" community on the kind
of digital camera to purchase and/or where to purchase a digital
camera. From the present disclosure, one of ordinary skill in the
art can see that there are a myriad of criteria that can be used to
identify those members that can be implicitly associated with the
user 112b. Those criteria merely depend on the extent of the
information contained in the member profiles for the member network
200 and the local search queries entered by the user 112b to obtain
endorsed local articles and/or ads.
[0065] At 380, the search engine 158 then merges the first and
second local search result sets together to form a final local
search result set. According to one embodiment of the present
invention, any endorsed local articles and/or ads in the final
search result set can be ranked differently from other local
articles in the set and annotated to indicate endorsements. The
ranking for each endorsed local article and/or ad can be based on
the type and/or degree of associations (implicit or explicit)
between the user 112b and the member in the member network 200 that
has endorsed such local article and/or ad. For instance, the final
search result set is the regular first local search result set that
has been re-ordered or re-ranked with article identifiers for the
endorsed local articles and/or ads (found from the second local
search result set) arranged at the top of the list with annotations
to indicate endorsements. The user 112b can then access the
annotations to view all endorsements for each endorsed local
article and/or ad and identify the user or users that made the
endorsements. This allows the user 112b to appropriately trust the
endorsements based on his/her associations (implicit or explicit)
with the endorsers. The annotations (e.g., "endorsed by John Doe"
and/or "from John Doe: a great place for buying widgets") can
accompany the article identifier of an endorsed local article or ad
and be placed in the vicinity of the article identifier.
Alternatively, the annotations can be links to pop-up windows or
other sites/pages that contain the endorsement language. According
to another embodiment of the present invention, the user 112b can
be automatically redirected to the navigational site of an endorsed
article or ad based on the ranking or re-ordering.
[0066] Although the aforementioned embodiments of the present
invention have been described with regard to query-dependent
endorsements, i.e., endorsed search results are provided based on
search queries submitted by a user, it should be understood that
query-independent endorsements also can be provided. FIGS. 6A-B
illustrate an exemplary method that provides query-independent
article endorsements whereby members can create and share their
personalized lists of articles with other members in the member
network and/or other users of a search engine. The exemplary method
is provided by way of example, as there are a variety of ways to
carry out methods according to the present invention. The method
shown in FIGS. 6A-B can be executed or otherwise performed by one
or a combination of various systems. The method in FIGS. 6A-B is
described below as carried out by the system 100 shown in FIG. 1 by
way of example, and various elements of the system 100 are
referenced in explaining the example method of FIGS. 6A-B.
[0067] The method begins at 610 and continues at 620 and 630 in a
similar manner as described earlier for 310, 320, and 330 in FIG.
3A, respectively.
[0068] At 640, similar to 340 in FIG. 3A, the user 112a can endorse
a local article by accessing the respective "endorse" link(s). For
instance, the user 112a can click on the "endorse" link and be
directed to a navigational page or window, wherein the user 112a is
presented with the option to either positively endorse (e.g.,
"Recommended") or negatively endorse (e.g., "Not Recommended") the
local article. In another instance, the user 112a can click on the
"endorse" link and be directed to a navigational page or window,
wherein the user 112a is presented with a scale, e.g., from 1 to 5
to rate the local article from best to worse or vice versa. In
still another instance, separately or in combination with the
aforementioned two instances, the user 112a can click on the
"endorse" link and be directed to a navigational page or window,
wherein the user 112a has the option to add comments about his/her
opinion of the local article.
[0069] Alternatively, the user 112a can endorse a local article
without having to first obtain the local article from a search
query. For instance, the user 112a can create a search endorsement
entry in his or her member profile, wherein, unlike 340 in FIG. 3A,
the user 112a does not have to input a desired search query for the
endorsement (because the endorsed search result set will be
query-independent anyway, although the user 112a can still input a
desired search query). Here, the user 112a also can input one or
more article identifiers of local articles that the user 112a
chooses to endorse, and the kind of endorsement for each of the
endorsed local article identifiers.
[0070] In a further instance, the user 112a does not click on the
available "endorse" link but click on the actual local article
identifier o navigate to the actual local article. The search
engine 158 then notes such access and transmits that information to
the member network site 160 for storage in the profile of the user
112a in the member network database 170. At some point in the
future, when the user 112a accesses the search engine 158 for
another search, and the search engine 158, communicating with the
member network engine 168, links the user 112a with his/her member
profile in the member network database 170, the search engine 158
can then prompt the user 112a to endorse the particular local
article that the user 112a previously accessed but did not endorse.
The user 112a can receive such prompt for endorsement the next time
he/she accesses the search engine 158 or after a predetermined
number of accesses to the search engine 158, Also, the user 112a
may be prompted one or more times as desired. If the user 112a
chooses to disregard the prompts, the search engine 158 can stop
prompting the user 112a to endorse such local article after a
predetermined number of prompts. Subsequently, the search engine
158 can bring up another article identifier for a local article
that the user 112a previously accessed but did not endorse and
repeat the above prompt process again.
[0071] At 645, similar to 345 in FIG. 3, the user 112a can also
endorse any number of ads in addition to or in lieu of the local
articles. As referred herein, an ad can be an online ad, such as a
banner ad, a pop-up window ad, or simply a listing of a name of a
product/service provider along with contact information. An ad can
also be in any format presently known or prospectively contemplated
in the art and accessible via its article identifier by the search
engine 158 and member network engine 168. According to one
embodiment of the present invention, the act of endorsing an ad is
separate from the act of accessing the search engine 158 to perform
a search as described above. Also, the endorsed ad may or may not
contain the same information as one of the local articles that the
search engine 158 can come up with for a particular local search
query. For instance, prior or subsequent to accessing the search
engine 158 for a search, the user 112a may have endorsed an ad
through a web site that handles such ad. The ad web site can then
forwards information about the endorsed ad to the member network
site 160 so that the member network engine 168 can store the
endorsed ad along with the proper local category for the ad in the
profile of the user 112a in the member network database 170. The
categorization of endorsed ads is similar to that for local
articles as described earlier.
[0072] At 650, similar to 350 in FIG. 3A, once the user 112a has
endorsed one or more local articles as represented by their article
identifiers in the local search result set and/or the user 112a has
endorsed one or more ads as also represented by their article
identifiers, the search engine 158 can communicate with the member
network engine 168 to store the endorsements in the member profile
in the member network database 170. The endorsements are
categorically stored in accordance with the category in which the
local search query submitted by the user 112a is listed.
[0073] Accordingly, the member profile of the user 112a can include
at least the following information: 1) an identifier of the user
112a (who can be anonymous); 2) a list of other users in the member
network 200 that have one or more associations with the user 112a
as described earlier; 3) a list of local categories that have local
search endorsements; 4) a list of endorsed local articles (e.g.,
names of a product/service providers and their contact information)
and/or ads in each of the categories previously submitted by the
user 112a; and/or 5) the endorsements by the user 112a for each of
the endorsed local articles and/or ads. Alternatively, the
endorsements can include the aforementioned information but be
stored in a file other than the member profile and yet be
associated with the member.
[0074] Referring now to FIG. 6B, the method continues at 660, which
is similar to 360 in FIG. 3 and described earlier. Next, in
response to receiving the query signal from the client device 102b
at 370, the search engine 158 performs two searches: 1) at 371, a
regular local search similar to 330 in FIG. 3A whereby the search
engine 158 locates the desired local information in a manner know
in the art and return article identifiers associated with the local
information in a first local search result set; and 2) at 372, a
search of the member network database 170 whereby the search engine
158 locates previously endorsed local articles and/or ads in
associated member profiles in a member network and return them in a
second local search result set. The search engine 158 searches the
member network database 170 by first communicating with the server
device 162 and its member network engine 168 to identify members in
the member network 200 that are associated with the user 112b.
Next, through the member network engine 168, the search engine 158
can access the member profiles of those associated members to look
up any available list of endorsed local articles and/or ads (via
their article identifiers) in the appropriate one or more
categories as described for those articles that match with the
article identifiers contained in the first search result set.
[0075] Thus, the user 112b can add a layer of trust on top of the
regular local search result set, even though such layer of trust
may be query-independent. The trust can be in the form of local
search endorsements from those members of the member network 200
that are associated with the user 112b because of their explicit
profile associations with the user 112b, as described earlier with
reference to FIG. 2. The trust can also be in the form of local
search endorsements from those members that are associated with the
user 112b because of their implicit profile associations with the
user 112b. In one implicit profile association, the search engine
158 and member network engine 168 can identify from member profiles
stored in the member network database 170 those members that have
the same or similar interests with the user 112b, based on a
comparison between the profile of the user 112b and profiles of
other users in the member network 200 or a comparison between the
profiles of other users in the member network 200 and the local
search query submitted by the user 112b. The search engine 158 and
member network engine 168 can then provide the user 112b with any
local article and/or ad endorsements that are classified in the
same category or categories with the local search query submitted
by the user 112b from such implicitly-associated members.
[0076] To provide an example, if the user 112b is searching for a
sushi restaurant in San Francisco, the user 112b can receive
endorsements from those members that live in the San Francisco area
and like Japanese or sushi restaurant or from those members that
are food critics in the San Francisco area, even though such
endorsements contained in the member profiles of those members may
not have associated any assigned search queries at all but merely
match some of the search results initially returned by the search
engine 158. To provide another example, if both the users 112a and
112b are members of a "photography" community within the member
network 200, and the user 112b is searching for a digital camera,
the user 112b can receive any endorsements from the user 112a and
other members of the "photography" community on the kind of digital
camera to purchase and/or where to purchase a digital camera. From
the present disclosure, one of ordinary skill in the art can see
that there are a myriad of criteria that can be used to identify
those members that can be implicitly associated with the user 112b.
Those criteria merely depend on the extent of the information
contained in the member profiles for the member network 200 as
entered by the user 112b to obtain endorsed local articles and/or
ads.
[0077] At 680, the method continues as described earlier for 380 of
FIG. 3.
[0078] FIGS. 3A and 6A have been described with reference to one
user, namely. user 112a, and FIGS. 3B and 6B have been described
with reference to another user, namely, user 112b. However, it
should be noted that a plurality of users, including the user 112a
or aside from the user 112a, may have endorsed a plurality of local
articles and/or ads in the same category or categories of the local
search query subsequently submitted by the user 112b. Also, the
users 112a and 112b can be one and the same. Hence, according to
one embodiment of the present invention, each article identifier in
the final search result set described in FIGS. 3B and 6B,
regardless of its endorsed or non-endorsed status, can be provided
with one or more "endorse" links that enables the user 112b to also
endorse and/or recommend the underlying local article or ad for the
submitted search query, just as the user 112a is able to do.
Further, the user 112b can still benefit from local search
endorsements by members of the member network 200 even when the
user 112b is not in a member network 200. This is because, as
mentioned earlier, the search engine 158 and the member network
engine 168 can retrieve endorsements from stored member profiles in
the member network database 170 that are implicitly associated with
the user 112b based on just the local search query submitted by the
user 112b.
[0079] Consequently, the local search endorsements can be used to
personalize the search engine's ranking of article identifiers in a
search result set by offering a way for users to re-rank the
article identifiers for themselves and for those associated with
them in the member network 200.
[0080] According to one embodiment of the present invention, the
local search endorsements can provide financial opportunities to
both the users who endorse local articles and/or ads and the search
site that implements the local search endorsements. For example, to
encourage users to endorse a particular article or ad for a
particular category, such web site can offer to pay each endorsing
user a financial proceed (e.g., a fee for each click through) for
the endorsement. The search site can also retain a portion of such
proceed.
EXAMPLE
[0081] Referring to the screenshot shown in FIG. 4, with reference
to FIGS. 1 and 2, an example in accordance with an embodiment of
the present invention is now provided. When a user A (who can be,
e.g., any one of the users 112a-n), having a profile A at vertex
202 in FIG. 2 submits, a search query for, e.g., widgets in
Chicago, to the search engine 158 at the search site 150, the
search engine returns a search result set as shown in FIG. 4 to the
user A. As shown, the article identifier 410 indicates an
endorsement of the underlying article with a comment by a user D
(having profile D at vertex 208 in FIG. 2). The article identifier
520 indicates a negative endorsement by a user E (having profile E
at vertex 410 in FIG. 3) with no comments. The article identifier
430 indicates endorsements by users B (having profile B at vertex
204 in FIG. 2) and C (having profile C at vertex 206 in FIG. 2)
with comments only from C. The article identifier 440 indicates an
endorsement by a user F (having profile F at vertex 212 in FIG. 2)
with no comments. The article identifier 450 indicates no
endorsements or comments.
[0082] From viewing the search results, the user A can decide to:
1) trust the articles represented by article identifiers 410 and
430 because they are endorsed by his/her friends, user B, C, and D
(as shown by the friendship associations 226, 32, and 222,
respectively); 2) trust the article represented by article
identifier 420 a little less because it is endorsed by his/her
friend of a friend, user E; and/or 3) not trust the articles
represented by article identifier 440 or 450 because article
identifier 440 is endorsed by a user F with whom the user A is
strenuously associated (a friend F of a friend G of a friend C) and
article identifier 450 is not endorsed by anyone. Alternatively, if
the user F is implicitly associated with the user A (e.g., the user
F is a Widget expert or aficionado in the Chicago area based on
his/her user profile, or both users A and F are members of widget
community or association within the member network 200), the user A
can choose to trust the article represented by article identifier
440. The user A can also trust the article represented by article
identifier 430 more than the article represented by article
identifier 410 because the user A is associated in more ways with
the user B than with the user D (three associations 224, 226, 228
versus two associations 220 and 222, as seen in FIG. 2).
[0083] As mentioned earlier, the user A also has an opportunity to
provide endorsements and/or comments to one or more of the articles
represented by article identifiers 410, 420, 430, 440, and 450 by
accessing the link 401 that accompanies each of the article
identifiers.
[0084] FIG. 5 shows a sample screenshot 500 of an endorsement page
that is provided when the user A accesses the link 401 of the
article identifier 410, in accordance with an embodiment of the
present invention. As shown, the user A can positively endorse the
link by clicking on button 510, negatively endorse the link by
clicking on button 520, and/or add comments in box 530. Once
finished, the user A can click on button 540 to end the endorsement
process, store the endorsement in his/her user profile in the
member network database 170, and return to the search result page
shown in FIG. 4. The endorsement by user A will then be added to a
search result page in a similar manner to that shown in FIG. 4.
General
[0085] Although the invention has been described with reference to
these embodiments, other embodiments could be made by those in the
art to achieve the same or similar results. Variations and
modifications of the present invention will be apparent to one
skilled in the art based on the present disclosure, and the present
invention encompasses all such modifications and equivalents.
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