U.S. patent application number 15/633696 was filed with the patent office on 2017-10-12 for real-time updates to item recommendation models based on matrix factorization.
This patent application is currently assigned to Amazon Technologies, Inc.. The applicant listed for this patent is Amazon Technologies, Inc.. Invention is credited to SAMUEL THEODORE SANDLER.
Application Number | 20170293865 15/633696 |
Document ID | / |
Family ID | 59069549 |
Filed Date | 2017-10-12 |
United States Patent
Application |
20170293865 |
Kind Code |
A1 |
SANDLER; SAMUEL THEODORE |
October 12, 2017 |
REAL-TIME UPDATES TO ITEM RECOMMENDATION MODELS BASED ON MATRIX
FACTORIZATION
Abstract
A network-based enterprise or other system that makes items
available for selection to users may implement real-time updates to
item recommendation models based on matrix factorization. An item
recommendation model may be maintained that is generated from a
singular value decomposition of a matrix indicating selections of
items by users. A user-specific update to the item recommendation
model may be calculated in real-time for a particular user such
that the calculation may be performed without performing another
singular value decomposition to generate an updated version of the
item recommendation model. Item recommendations may then be made
based on the user-specific update and the item recommendation
model. In various embodiments, the item recommendations may be made
in response to an indication or request for item recommendations
for the particular user.
Inventors: |
SANDLER; SAMUEL THEODORE;
(SEATTLE, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Amazon Technologies, Inc. |
Seattle |
WA |
US |
|
|
Assignee: |
Amazon Technologies, Inc.
Seattle
WA
|
Family ID: |
59069549 |
Appl. No.: |
15/633696 |
Filed: |
June 26, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14288216 |
May 27, 2014 |
9691035 |
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15633696 |
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61979380 |
Apr 14, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0631 20130101;
G06Q 30/0241 20130101 |
International
Class: |
G06N 99/00 20060101
G06N099/00; G06N 5/04 20060101 G06N005/04 |
Claims
1-21. (canceled)
22. A system, comprising: at least one processor; and a memory,
storing program instructions that when executed by the at least one
processor cause the at least one processor to: identify two or more
linked users of a plurality of users; obtain respective user
parameter vectors for the linked users and one or more respective
item parameter vectors for one or more items selected by linked
users from an item recommendation model, wherein the item
recommendation model comprises a user matrix and an item matrix
that are generated from a matrix factorization of a single matrix
indicating respective item selections for individual ones of a
plurality of users with regard to a plurality of items, wherein the
one or more items were selected by the linked users after the
generation of the item recommendation model; calculate respective
user-specific updates to the item recommendation model for the
linked users based, at least in part, on the respective user
parameter vectors and the respective item parameter vectors; and
determine at least one item recommendation for at least one of the
linked users based, at least in part, on the user-specific updates
to the item recommendation model.
23. The system of claim 22, wherein to identify the two or more
linked users the program instructions cause the at least one
processor to evaluate one or more contacts of one of the linked
users to identify other ones of the linked users.
24. The system of claim 22, wherein the linked users are associated
with a same user account of a network-based enterprise.
25. The system of claim 22, wherein to determine at least one item
recommendation for at least one of the linked users the program
instructions cause the at least one processor to: generate
respective candidate item recommendations for the linked users
according to the user-specific updates to the item recommendation
model for the linked users; and compare the respective candidate
item recommendations for the linked users to select one or more of
the respective candidate item recommendations as the at least one
item recommendation for the at least one linked user.
26. The system of claim 22, wherein to calculate the respective
user-specific updates to the item recommendation model for the
linked users, the program instructions cause the at least one
processor to: for individual ones of the user-specific updates,
perform vector addition to combine the user parameter vector of one
of the linked users and the respective item parameter vectors of
the items selected by the linked user after the generation of the
item recommendation model to calculate the user-specific update for
the linked user.
27. The system of claim 22, wherein to calculate the respective
user-specific updates to the item recommendation model for the
linked users, the program instructions cause the at least one
processor to: for individual ones of the user-specific updates,
perform vector addition to combine the user parameter vector of two
or more of the linked users and the respective item parameter
vectors of the items selected by the two or more linked users after
the generation of the item recommendation model to calculate the
user-specific update for one of the linked users.
28. The system of claim 22, wherein the program instructions
further cause the at least one processor to: receive an item
recommendation request for a particular user; in response to the
receipt of the request: perform the identification, the obtain, the
calculation, and the determination, wherein the particular user is
the at least one linked user; and respond to the request with the
at least one item recommendation.
29. A method, comprising: performing, by one or more computing
devices: identifying two or more linked users of a plurality of
users; obtaining respective user parameter vectors for the linked
users and one or more respective item parameter vectors for one or
more items selected by linked users from an item recommendation
model, wherein the item recommendation model comprises a user
matrix and an item matrix that are generated from a matrix
factorization of a single matrix indicating respective item
selections for individual ones of a plurality of users with regard
to a plurality of items, wherein the one or more items were
selected by the linked users after the generation of the item
recommendation model; calculating respective user-specific updates
to the item recommendation model for the linked users based, at
least in part, on the respective user parameter vectors and the
respective item parameter vectors; and determining at least one
item recommendation for at least one of the linked users based, at
least in part, on the user-specific updates to the item
recommendation model.
30. The method of claim 29, wherein identifying the two or more
linked users comprises evaluating one or more contacts of one of
the linked users to identify other ones of the linked users.
31. The method of claim 29, wherein the linked users are associated
with a same user account of a network-based enterprise.
32. The method of claim 29, wherein determining at least one item
recommendation for at least one of the linked users, comprises:
generating respective candidate item recommendations for the linked
users according to the user-specific updates to the item
recommendation model for the linked users; and comparing the
respective candidate item recommendations for the linked users to
select one or more of the respective candidate item
recommendations.
33. The method of claim 29, wherein calculating the respective
user-specific updates to the item recommendation model for the
linked users comprises: for individual ones of the user-specific
updates, performing vector addition to combine the user parameter
vector of one of the linked users and the respective item parameter
vectors of the items selected by the linked user after the
generation of the item recommendation model to calculate the
user-specific update for the linked user.
34. The method of claim 29, wherein calculating the respective
user-specific updates to the item recommendation model for the
linked users comprises: for individual ones of the user-specific
updates, performing vector addition to combine the user parameter
vector of two or more of the linked users and the respective item
parameter vectors of the items selected by the two or more linked
users after the generation of the item recommendation model to
calculate the user-specific update for one of the linked users.
35. The method of claim 29, further comprising: receiving an item
recommendation request for a particular user; in response to
receiving the request: performing the identifying, the obtaining,
the calculating, and the determining, wherein the particular user
is the at least one linked user; and responding to the request with
the at least one item recommendation.
36. A non-transitory computer-readable storage medium, storing
program instructions that when executed by one or more computing
devices cause the one or more computing devices to implement:
identifying two or more linked users of a plurality of users;
obtaining respective user parameter vectors for the linked users
and one or more respective item parameter vectors for one or more
items selected by linked users from an item recommendation model,
wherein the item recommendation model comprises a user matrix and
an item matrix that are generated from a matrix factorization of a
single matrix indicating respective item selections for individual
ones of a plurality of users with regard to a plurality of items,
wherein the one or more items were selected by the linked users
after the generation of the item recommendation model; calculating
respective user-specific updates to the item recommendation model
for the linked users based, at least in part, on the respective
user parameter vectors and the respective item parameter vectors;
and determining at least one item recommendation for at least one
of the linked users based, at least in part, on the user-specific
updates to the item recommendation model.
37. The non-transitory, computer-readable storage medium of claim
36, wherein, in identifying the two or more linked users, the
program instructions cause the one or more computing devices to
implement evaluating one or more contacts of one of the linked
users to identify other ones of the linked users.
38. The non-transitory, computer-readable storage medium of claim
36, wherein, in determining at least one item recommendation for at
least one of the linked users, the program instructions cause the
one or more computing devices to implement: generating respective
candidate item recommendations for the linked users according to
the user-specific updates to the item recommendation model for the
linked users; and comparing the respective candidate item
recommendations for the linked users to select one or more of the
respective candidate item recommendations.
39. The non-transitory, computer-readable storage medium of claim
36, wherein, in calculating the respective user-specific updates to
the item recommendation model for the linked users, the program
instructions cause the one or more computing devices to implement:
for individual ones of the user-specific updates, performing vector
addition to combine the user parameter vector of one of the linked
users and the respective item parameter vectors of the items
selected by the linked user after the generation of the item
recommendation model to calculate the user-specific update for the
linked user.
40. The non-transitory, computer-readable storage medium of claim
36, wherein, in calculating the respective user-specific updates to
the item recommendation model for the linked users, the program
instructions cause the one or more computing devices to implement:
for individual ones of the user-specific updates, performing vector
addition to combine the user parameter vector of two or more of the
linked users and the respective item parameter vectors of the items
selected by the two or more linked users after the generation of
the item recommendation model to calculate the user-specific update
for one of the linked users.
41. The non-transitory, computer-readable storage medium of claim
36, wherein the program instructions cause the one or more
computing devices to further implement: receiving an item
recommendation request for a particular user; in response to
receiving the request: performing the identifying, the obtaining,
the calculating, and the determining, wherein the particular user
is the at least one linked user; and responding to the request with
the at least one item recommendation.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 14/288,216, filed May 27, 2014, now U.S. Pat.
No. 9,691,035, which claims benefit of priority to U.S. Provisional
Application Ser. No. 61/979,380, entitled "Real-Time Updates to
Item Recommendation Models Based on Matrix Factorization," filed
Apr. 14, 2014, and which is incorporated herein by reference in its
entirety.
BACKGROUND
[0002] Consumer choices for goods and services have grown
exponentially upon the advent of the digital age. E-commerce,
content distribution networks, and other communication technologies
have enabled customers to choose from many more goods than were
previously available to them. However, navigating the sheer number
products now available can prove daunting and ultimately discourage
some customers from making purchases using these new means.
Recommendation systems have been developed in order to provide
customers with some assistance when choosing new products,
especially if these products are not physically available to the
customer at the moment when a purchase, selection, or ordering
decision is made. A recommendation system may provide feedback or
recommended items to a customer so that the customer may make a
more informed decision as to whether or not an item may be a good
purchase.
[0003] In order to create effective item recommendation systems,
large amounts of past behavior of customers may be tracked and
maintained. This customer data may be analyzed in order to make
suggestions of items that, for example, other similar customers
have purchased. As the amount of data used in generating item
recommendations continues to grow however, these recommendation
systems may become less agile. Recent purchases, views, or other
selections may not be accounted for as many item recommendation
techniques evaluate large sets of data in accordance with processes
that take significant amounts of time.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 is a block diagram illustrating a real-time item
recommendation engine implementing real-time updates to an item
recommendation model based on matrix factorization, according to
some embodiments.
[0005] FIG. 2A is a block diagram illustrating an item
recommendation model based on matrix factorization, according to
some embodiments.
[0006] FIG. 2B is a scatter plot diagram comparing common item
recommendations between a real-time update for an item
recommendation model for users and an item recommendation model
that is not updated in real-time for the same users, according to
some embodiments.
[0007] FIG. 2C is a scatter plot diagram comparing user parameter
vector differences between a real-time update for an item
recommendation model for users and an item recommendation model
that is not updated in real-time for the same users, according to
some embodiments.
[0008] FIG. 3A is a block diagram illustrating a network-based
enterprise implementing real-time updates to an item recommendation
model based on matrix factorization, according to some
embodiments.
[0009] FIG. 3B is a block diagram illustrating a customer device
implementing real-time updates to an item recommendation model
based on matrix factorization, according to some embodiments.
[0010] FIG. 4 is a sequence diagram illustrating determining item
recommendations for a particular user based on a real-time update
to an item recommendation model in response to an item
recommendation request for the particular user, according to some
embodiments.
[0011] FIG. 5 is a high-level flowchart illustrating methods and
techniques for implementing real-time updates to an item
recommendation model based on matrix factorization, according to
some embodiments.
[0012] FIG. 6 is a high-level flowchart illustrating methods and
techniques for calculating a user-specific update for an item
recommendation model for a particular user, according to some
embodiments.
[0013] FIG. 7 is high-level flowchart illustrating methods and
techniques for calculating a user-specific update for a particular
user linked to other users, according to some embodiments.
[0014] FIG. 8 is a high-level flowchart illustrating methods and
techniques for comparing item recommendations among a particular
user and other users linked to the particular user, according to
some embodiments.
[0015] FIG. 9 is a high-level flowchart illustrating methods and
techniques for determining one or more item recommendations based
on an item recommendation model generated from matrix
factorization, according to some embodiments.
[0016] FIG. 10 is an example computer system, according to various
embodiments.
[0017] While embodiments are described herein by way of example for
several embodiments and illustrative drawings, those skilled in the
art will recognize that the embodiments are not limited to the
embodiments or drawings described. It should be understood, that
the drawings and detailed description thereto are not intended to
limit embodiments to the particular form disclosed, but on the
contrary, the intention is to cover all modifications, equivalents
and alternatives falling within the spirit and scope as defined by
the appended claims. The headings used herein are for
organizational purposes only and are not meant to be used to limit
the scope of the description or the claims. As used throughout this
application, the word "may" is used in a permissive sense (i.e.,
meaning having the potential to), rather than the mandatory sense
(i.e., meaning must). The words "include," "including," and
"includes" indicate open-ended relationships and therefore mean
including, but not limited to. Similarly, the words "have,"
"having," and "has" also indicate open-ended relationships, and
thus mean having, but not limited to. The terms "first," "second,"
"third," and so forth as used herein are used as labels for nouns
that they precede, and do not imply any type of ordering (e.g.,
spatial, temporal, logical, etc.) unless such an ordering is
otherwise explicitly indicated.
[0018] Various components may be described as "configured to"
perform a task or tasks. In such contexts, "configured to" is a
broad recitation generally meaning "having structure that" performs
the task or tasks during operation. As such, the component can be
configured to perform the task even when the component is not
currently performing that task (e.g., a computer system may be
configured to perform operations even when the operations are not
currently being performed). In some contexts, "configured to" may
be a broad recitation of structure generally meaning "having
circuitry that" performs the task or tasks during operation. As
such, the component can be configured to perform the task even when
the component is not currently on. In general, the circuitry that
forms the structure corresponding to "configured to" may include
hardware circuits.
[0019] Various components may be described as performing a task or
tasks, for convenience in the description. Such descriptions should
be interpreted as including the phrase "configured to." Reciting a
component that is configured to perform one or more tasks is
expressly intended not to invoke 35 U.S.C. .sctn.112, paragraph
six, interpretation for that component.
[0020] "Based On." As used herein, this term is used to describe
one or more factors that affect a determination. This term does not
foreclose additional factors that may affect a determination. That
is, a determination may be solely based on those factors or based,
at least in part, on those factors. Consider the phrase "determine
A based on B." While B may be a factor that affects the
determination of A, such a phrase does not foreclose the
determination of A from also being based on C. In other instances,
A may be determined based solely on B.
[0021] The scope of the present disclosure includes any feature or
combination of features disclosed herein (either explicitly or
implicitly), or any generalization thereof, whether or not it
mitigates any or all of the problems addressed herein. Accordingly,
new claims may be formulated during prosecution of this application
(or an application claiming priority thereto) to any such
combination of features. In particular, with reference to the
appended claims, features from dependent claims may be combined
with those of the independent claims and features from respective
independent claims may be combined in any appropriate manner and
not merely in the specific combinations enumerated in the appended
claims.
DETAILED DESCRIPTION
[0022] Various embodiments of real-time updates to item
recommendation models based on matrix factorization are described
herein. Item recommendation models typically contain large data
sets describing the actions of many users with respect to various
items. In order to efficiently analyze the large amounts of data
collected and retained for the purpose of generating item
recommendation models matrix factorization techniques may be
implemented. Singular value decomposition, for example, takes data
representing item selections by users in a single large matrix and
generates corresponding matrices for the users and the items, which
may then be used to make item recommendations as the item
recommendation model. However, techniques such as singular value
decomposition are often performed as a batch process, working upon
all existing data for items selections as of a current time. This
technique may not be performed efficiently for providing item
recommendations for particular users in real-time. Real-time
updates to item recommendation models, however, may be implemented
that provide item recommendations with similar results to
performing an update of the entire item recommendation model.
[0023] FIG. 1 is a block diagram illustrating a real-time item
recommendation engine implementing real-time updates to an item
recommendation model based on matrix factorization, according to
some embodiments. Real-time recommendation engine 100 may be
configured to provide item recommendations for particular users in
real-time, without having to generate an update to an entire item
data model. In various embodiments, items may be any digital
product or media, physical product, service, or other object
physical or virtual for which a selection action may be performed.
Persistent data store 180 (e.g., persistent block-based storage
devices such as hard disk drives or solid state drives) may store
item selection data 110 and an item recommendation model 130. As
selections of items are made by users, these selections 106 may be
stored in item selection data 110. Periodically (or aperiodically)
item recommendation model 130 may be generated from item selection
data. Recommendation model generator 120 (which may be implemented
as part of real-time item recommendation engine 100) may receive as
input the item selection data 110 up to a certain point in time.
The data may be formatted as a single matrix indicating the
selection of items by particular users. Recommendation model
generator may perform a singular value decomposition (as described
in detail below with regard to FIG. 2A) to generate the item
recommendation model 130, which may be associated with a particular
point in time. This may result in a user matrix storing user
parameter vectors for users and an item matrix storing item
parameter vectors for items.
[0024] As user recommendation requests are received for particular
users 102, user-specific recommendation update module 150 may be
configured to obtain information about a particular user's item
selections. Those item selections that occurred after the
particular point in time in which the item recommendation model 130
was generated may be identified. User-specific recommendation
update module 150 may also be configured to access the item
recommendation model 130 to obtain a user parameter vector for the
particular user and item parameter vectors for those items that
were subsequently selected by the user. In some embodiments, other
user parameter vectors and/or item parameter vectors selected by
other users linked to the particular user may be selected and used
for determining a user-specific update for the item recommendation
model for the particular user. The obtained parameter vectors may
be combined using vector addition to generate an updated user
parameter vector for the particular user. Further detail is
described below with regard to FIGS. 5-7. This user-specific update
may then be made available to factorization-based item
recommendation module 160 to determine item recommendations for the
particular user. For example, factorization based item
recommendation module 160 may be configured to obtain other item
parameter vectors from item recommendation model 130 in order to
calculate affinity scores for the particular user based on the
user-specific update (as described in more detail below with regard
to FIG. 9).
[0025] In some embodiments, a recommendation filter module 170 may
be configured to select particular items to recommend based on
selection criteria. For example, filter module 170 may obtain past
item selections from item selection data 110 for the particular
user in order to prevent previously selected items by the
particular user from being recommended to the particular user. User
item recommendations may then be provided 104 to the particular
user, for example via a network-based interface.
[0026] Please note, FIG. 1 is provided as a logical illustration of
real-time updates to item recommendation models based on matrix
factorization, and are not intended to be limiting as to the
physical arrangement, size, or number of components, modules, or
devices, implementing a real-time recommendation engine or data
store. For example, in some embodiments, the item recommendation
model 130 and item selection data 110 may be stored separately. In
another example, the module for generating the item recommendation
module may be implemented separately from the real-time item
recommendation engine.
[0027] FIG. 2A is a block diagram illustrating an item
recommendation model based on matrix factorization, according to
some embodiments. Item selection data 110 may maintain, in various
embodiments, a single matrix describing user selections with regard
to items, matrix A 202. This item selection matrix may be
represented as A.epsilon.[0,1]m.times.n where m is the number of
users and n is the number of items. A.sub.ij=1 if user i selected
item j. Recommendation model generator 120 may be implement a
component 210 configured to perform a singular value decomposition
on item selection data, matrix A 202 in order to generate the item
recommendation model. For example, A=U.SIGMA.V.sup.T be the thin
singular value decomposition (SVD) of A where d is the latent
dimensionality. The shapes of the matrices may be U, .SIGMA., V are
U.epsilon.R.sup.m.times.d, V.epsilon.R.sup.n.times.d, and
.SIGMA..epsilon..sup.d.times.d diagonal. The columns of U and V are
orthogonal and have unit norm. Therefore, Q=U.SIGMA. may be user
matrix Q 222, whose i'th row may be the parameter vector for the
i'th user. The diagonal matrix scales the columns of U by the
corresponding diagonal element Q.sub.ij=U.sub.ij.SIGMA..sub.ij.
Item matrix V 224 may be the matrix of video parameters whose j'th
row is the parameter vector for item j. Thus, an affinity estimate
of a user is affinity for item j may be
.SIGMA..sub.kq.sub.tkV.sub.j.
[0028] From an SVD decomposition of the single matrix A, 202, and
as V is orthogonal, it may be that Q=AV. Moreover, as A is a binary
matrix, the i'th row of Q is the just sum of the rows V.sub.j for
which A.sub.ij=1.
Q j ' = j = 1 d A ij V j ' = j ' Aij = 1 V j ' ##EQU00001##
In other words, the parameter vector for a user is may be the sum
of the parameter vectors for the items selected. Therefore, a
user-specific update may be calculated for user selections up to
time t, so that Q.sub.t+=V.sub.j, for all items selected since time
t. In this way, the SVD calculation to generate the item
recommendation model may be updated for a specific user without
having to perform SVD. This may allow for longer intervals between
generating an updated item recommendation model 130.
[0029] While user-specific updates may not account for all changes
that generating an updated item recommendation model may include,
user-specific updates may produce similar results. FIG. 2B is a
scatter plot diagram comparing common item recommendations between
a real-time update for an item recommendation model for users and
an item recommendation model that is not updated in real-time for
the same users, according to some embodiments. Scatter chart 230
illustrates on the x-axis an older version of the item
recommendation model. The y-axis represents recommendations using
user-specific updates and the older item recommendation model. 200
test recommendations were made to compare the results of the two
techniques with a "perfect" model that includes all item
selections. Each point represents recommendations for a particular
user. For example, point 232 represents that the older version of
the model had 0 recommendations in common with the "perfect" model,
while the older version plus the user-specific updates had
approximately 125 recommendations in common with the "perfect"
model.
[0030] FIG. 2C is a scatter plot diagram comparing user parameter
vector differences between a real-time update for an item
recommendation model for users and an item recommendation model
that is not updated in real-time for the same users, according to
some embodiments. Again, scatter chart 240 illustrates on the
x-axis an older version of the item recommendation model. The
y-axis represents recommendations using user-specific updates and
the older item recommendation model. 200 test recommendations were
made to compare the results of the two techniques with a "perfect"
model that includes all item selections. Each point represents
distance between the "perfect" model's user parameter vectors with
the parameter vector for a particular user using the older version
of the item recommendation model alone and the older item
recommendation model including user-specific updates. Point 242
illustrates that the user-specific update parameter has almost no
distance from the "perfect" model (thus being very similar) than
the older model without user updates, showing a distance of near
0.7.
[0031] The systems described herein may, in some embodiments,
implement a network-based enterprise that offers items to customers
(which may be users). The items may be used, purchased, rented, or
otherwise consumed. Selection of an item may correspond to one of
these actions and/or rating or reviewing a particular item. Some
embodiments of a network-based enterprise system are illustrated in
FIG. 3A. In the illustrated embodiment, a number of clients (shown
as customers 350a-350n) may be configured to interact with a
network-based enterprise 300 via a network 360. An enterprise
interface (e.g., a network-based site, such as a website) 310 may
handle or communicate with customers 350. Enterprise business logic
320 may be configured to handle processing, management, and other
techniques necessary to provide the request items to customers 350.
Enterprise data store 330 may maintain information for the
network-based enterprise 300. Item fulfillment 340 may be either
the physical or digital resources (items) provided to customers 350
upon selection. It is noted that where one or more instances of a
given component may exist, reference to that component herein may
be made in either the singular or the plural. However, usage of
either form is not intended to preclude the other.
[0032] In various embodiments, the components illustrated in FIG.
3A may be implemented directly within computer hardware, as
instructions directly or indirectly executable by computer hardware
(e.g., a microprocessor or computer system), or using a combination
of these techniques. For example, the components of FIG. 3A may be
implemented by a system that includes a number of computing nodes
(or simply, nodes), each of which may be similar to the computer
system embodiment illustrated in FIG. 10 and described below. In
various embodiments, the functionality of a given system component
(e.g., real-time item recommendation engine 100) may be implemented
by a particular node or may be distributed across several nodes. In
some embodiments, a given node may implement the functionality of
more than one system component (e.g., more than enterprise business
logic 320 component).
[0033] Generally speaking, customers 350 may encompass any type of
client or other component configurable to submit network-based
requests to network-based enterprise 300 via network 360, including
requests to select particular items offered. For example, a given
customer 350 may include a suitable version of a web browser, or
may include a plug-in module or other type of code module
configured to execute as an extension to or within an execution
environment provided by a web browser. For example, selection of
physical products for purchase, submitting payment information and
shipping information may be conveyed via the web browser.
Alternatively, a customer 350 (e.g., a gaming client) may encompass
an application such as a gaming application (or user interface
thereof), a media application, an office application or any other
application that may make use of persistent storage resources to
store and/or access digital items. In some embodiments, such an
application may include sufficient protocol support (e.g., for a
suitable version of Hypertext Transfer Protocol (HTTP)) for
generating and processing network-based requests without
necessarily implementing full browser support for all types of
network-based data. That is, customer 350 may be an application
configured to interact directly with network-based enterprise 300
(or enterprise interface 310). In some embodiments, customer 350
may be configured to generate network-based requests according to a
Representational State Transfer (REST)-style network-based
architecture, a document- or message-based network-based
architecture, or another suitable network-based architecture.
[0034] Customers 350 may convey network-based requests (e.g., item
selection requests) to and receive responses from network-based
enterprise 300 via network 360. In various embodiments, network 360
may encompass any suitable combination of networking hardware and
protocols necessary to establish network-based-based communications
between customer 350 and network-based enterprise 300 (and/or
enterprise interface 310). For example, network 360 may generally
encompass the various telecommunications networks and service
providers that collectively implement the Internet. Network 360 may
also include private networks such as local area networks (LANs) or
wide area networks (WANs) as well as public or private wireless
networks. For example, both a given customer 350 and network-based
enterprise 300 may be respectively provisioned within enterprises
having their own internal networks. In such an embodiment, network
360 may include the hardware (e.g., modems, routers, switches, load
balancers, proxy servers, etc.) and software (e.g., protocol
stacks, accounting software, firewall/security software, etc.)
necessary to establish a networking link between given customer 350
and the Internet as well as between the Internet and network-based
enterprise 300. It is noted that in some embodiments, customer 350
may communicate with network-based enterprise 300 using a private
network rather than the public Internet. For example, customer 350
may via a private network as part of selecting and receiving
digital items offered by network-based enterprise 300. In such a
case, customers 350 may communicate with enterprise 300 entirely
through a private network 360 (e.g., a LAN or WAN that may use
Internet-based communication protocols but which is not publicly
accessible).
[0035] Generally speaking, network-based enterprise 300 may be
configured to implement enterprise interface 310 which may be
configured to receive and process network-based requests, such as
requests to select, browse, access, or otherwise interact with
items offered. For example, enterprise interface 310 may include
hardware and/or software configured to implement a network-based
site, such that a web browser or other component implemented on
customer 350 may be configured to receive information via the
network-based site. For example, in some embodiments, real-time
item recommendations 312 may be provided to different customers 350
(e.g., based on a user associated with the customer, such as by a
user or customer identifier or account number) via the
network-based site. Enterprise interface 310 may be implemented as
a server system configured to receive network-based requests from
customers 350 and to forward them to components of a system, such
as enterprise business logic 320, that facilitate the offering,
sale, distribution or other functionalities of the items offered by
network-based enterprise 300. In other embodiments, enterprise
interface 310 may be configured as a number of distinct systems
(e.g., in a cluster topology) implementing load balancing and other
request management features configured to dynamically manage
large-scale network-based request processing loads. In various
embodiments, enterprise interface 310 may be configured to support
REST-style or document-based (e.g., SOAP-based) types of
network-based requests.
[0036] Enterprise business logic 320 may be configured to
facilitate the operations of network-based enterprise 300. For
example, enterprise business logic 320 may coordinate the purchase,
rental, access, sharing, metering and/or accounting of client
usage/selection of items, which may be services, physical products,
or digital media, in various embodiments. In at least some
embodiments, network-based enterprise 300 may be a streaming video
service. Enterprise business logic 320 may implement financial
accounting and billing systems, or may maintain a database of usage
data that may be queried and processed by external systems for
reporting and billing of customer activity. In certain embodiments,
enterprise business logic 320 may be configured to collect, monitor
and/or aggregate a variety of operational metrics, such as metrics
reflecting the rates and types of requests received from customers
350, bandwidth utilized by such requests, system processing latency
for such requests, system component utilization (e.g., network
bandwidth and/or storage utilization within the storage service
system), rates and types of errors resulting from requests or any
other suitable metrics. In some embodiments such metrics may be
used by system administrators to tune and maintain system
components, while in other embodiments such metrics (or relevant
portions of such metrics) may be exposed to customers 350 to enable
such customers to monitor their usage of services/items. Enterprise
business logic may also implement various user and/or customer
account functions which may be responsible for updating or
maintaining customer/user account information. User information,
such as a unique user identifier, may be linked to item selection
data 110 for customers/users maintained in enterprise data store
330.
[0037] In some embodiments, enterprise business logic 320 may also
implement user authentication and access control procedures. For
example, for a given network-based request to access a particular
item, enterprise business logic 320 may be configured to ascertain
whether the customer 350 associated with the request is authorized
to access the particular item. Enterprise business logic 320 may
determine such authorization by, for example, evaluating an
identity, password or other credential against credentials
associated with the particular item, or evaluating the requested
access to the particular item against an access control list for
the particular item. Various access control policies may be stored
as records or lists of access control information by enterprise
business logic 320. In some embodiments, these access control
policies may be implemented to accept or deny access to multiple
items offered by network-based enterprise 300 (e.g., some or all of
streaming videos).
[0038] Enterprise data store 330 may be one or more storage nodes,
systems, or servers configured to persistently store data for
enterprise data store, such as the aforementioned user information,
item selection data 110 and/or item recommendation model 130.
Various durability and/or security techniques may be implemented to
ensure safe and reliable storage of sensitive information, such as
payment information, accounts, or passwords.
[0039] Item fulfillment 340 may be one or more systems or devices
configured to provide selected items to customers 350 that are
offered by network-based enterprise 300. For example, item
fulfillment 340 may be a network of one or more fulfillment centers
that stock physical products and process shipment orders of those
products selected by customers 350. In some embodiments, item
fulfillment 349 may be application servers, content distribution
networks, application, gaming or other media platforms that provide
access to or a copy of digital media selected by customers 350. For
example, movies, television shows, or other audio/visual media may
be streamed or downloaded to customers 350 for consumption as part
of a network-based video streaming or gaming service. If access to
or rights to an item is limited, item fulfillment 340 may be
configured to enforce digital rights management (DRM) or other
controls to enforce the policies of the items and their respective
offers (e.g., rental or borrow of digital media for certain time
periods).
[0040] As illustrated in FIG. 3A, real-time recommendation engine
100 may be implemented in enterprise business logic 320 in order to
provide item recommendations for customers 350. For example, a
particular customer 350 may be communicating with the enterprise
interface, perhaps making a selection of a particular item.
Real-time product recommendation engine may receive a request from
another component of business logic 320 or enterprise interface 310
to provide real-time item recommendations for the particular user
that selected the particular item. Item selection data 110 may be
accessed to identify subsequent item selections after the
generation of item recommendation model 130. A user-specific update
for the user may be calculated based on a user parameter vector
obtained from item recommendation model 130 as well as item
parameter vectors for the subsequently selected items. Item
recommendations may then be determined, according to the various
techniques described in FIGS. 5-8 below, in order to provide the
determined item recommendations 312 for the particular user.
[0041] FIG. 3B is a block diagram illustrating a customer device
implementing real-time updates to an item recommendation model
based on matrix factorization, according to some embodiments.
Instead of implementing real-time item recommendation engine 100 as
part of the network-based enterprise 300 (as described above with
regard to FIG. 3A), customer device 370 may implement real-time
recommendation engine 100, in various embodiments. Item
recommendations may be made similar to the techniques described
above. Local item recommendation model 372 may be a recommendation
model that is received from network-based service 300, which may
implement a model generator 322 to provide update versions of the
item recommendation model including selections up to a particular
point in time. Real-time recommendation engine 100 may provide
real-time item recommendations accessing user parameter vectors and
item vectors maintained in local item recommendation model 372. In
at least some embodiments, real-time item recommendations engine
100 may access other actions taken at customer device 370. For
example, customer device 370 may be a streaming media device that
is configured to access or select media offerings from multiple
different network-based enterprises. Real-time item recommendations
engine may be configured to track other selections of items offered
by these other network-based services and map them to particular
items offered by network-based enterprise 300. If, for instance,
movie A is selected from another network-based enterprise and
watched at customer device 370, the real-time item recommendation
engine 100 may be configured to map the selection of movie A to
(either movie A offered via network-based enterprise 300 or a
similar movie) and include the corresponding item parameter vector
for movie A in calculating user-specific updates.
[0042] FIG. 4 is a sequence diagram illustrating determining item
recommendations for a particular user based on a real-time update
to an item recommendation model in response to an item
recommendation request for the particular user, according to some
embodiments. Real-time recommendation engine 420 may provide item
recommendations for a particular user in real-time, by determining
user-specific updates to an item recommendation model. Data store
430 may store data such as the item recommendation model (e.g.,
item recommendation model 130 and item selection data 110 discussed
above). Real-time recommendation engine 420 (or other responsible
component) may generate a new version of the item recommendation
model 440 at data store 430 at a particular point in time.
[0043] Item selections may be made users subsequent to the
generation or update to the item recommendation model 440. For
example, a particular user may make user selections 442, 444, and
446 of items x, y and z after the generation of item recommendation
model 440. Indications of these item selections may be stored in
item selection data in data store 430. When item recommendation
request 448 is received at real-time recommendation engine 420 for
the particular user these subsequent item selections may be
accounted for. For example real-time recommendation engine 450 may
access user item selections 450 for the particular user, to obtain
the user item selections 452. Real-time recommendation engine 420
may then identify 454 those user selections that occurred after the
generation of the item recommendation model 440 (e.g., by comparing
timestamps of the user selections). Thus, user selections of items
x, y and z (442, 444 and 446) may be identified.
[0044] Real-time recommendation engine 420 may then access data
store 430 to obtain parameter vectors from the item recommendation
model 456. Parameter vectors for the particular user (user i) and
the particular items (x, y and z) may be returned 458. The
user-specific update may then be calculated 460, such as by
performing vector addition to combine the user parameter vector
q.sub.i with the item parameter vectors v.sub.x, v.sub.y and
v.sub.z. Real-time recommendation engine 420 may then access data
store 430 to obtain other item parameter vectors for determining
item recommendations 462. Real-time recommendation engine 420 may
then make item recommendation determinations 466. For instance,
affinity scores may be determined for the particular user and
various items by performing a dot product calculation between the
user-specific update and a respective item parameter vector. Then,
real-time recommendation engine 420 may respond with the item
recommendations 468.
[0045] As discussed above, the user-specific update to the item
recommendation model may be performed in real-time, without
resorting to performing another singular value decomposition to
update the entire item recommendation model. The dotted line 470
illustrates visually a timespan for which a singular value
decomposition to update the item recommendation model may be
performed. Note the break in the time, indicating the additional
time not illustrated that may be required to perform a complete
update to the item recommendation model. Thus, user-specific
updates useful for providing real-time item recommendations may be
made in significantly less time than an update to the entire item
recommendation model.
[0046] The various embodiments of a network-based enterprise
implementing real-time updates to item recommendation models based
on matrix factorization described with regard to FIGS. 2-4 above,
may implement one or more different techniques described below with
regard to FIGS. 5-8. However, various other kinds of item
recommendation systems may implement real-time updates to item
recommendation models based on matrix factorization. FIG. 5 is a
high-level flowchart illustrating methods and techniques for
implementing real-time updates to an item recommendation model
based on matrix factorization, according to some embodiments.
Different combinations of systems and/or devices may implement the
various techniques discussed below.
[0047] As indicated at 510, access to an item recommendation model
generated from a singular value decomposition of a single matrix
indicating user selection of items at a particular point in time
may be obtained, in various embodiments. The item recommendation
model may include a user matrix, of which particular rows in the
user matrix represent particular users. The item recommendation
model may also include an item matrix, of which particular rows in
the item matrix may represent particular items. The item
recommendation model may be maintained persistently, in various
embodiments.
[0048] In at least some embodiments, an item recommendation request
for a particular user may be received, as indicated at 520. For
instance, a network-based interface or other component or module
may desire to provide item recommendations to a particular user and
send a request to a system component, such as real-time item
recommendation engine 100 described above with regard to FIG. 1, to
provide item recommendations. Please note, however, that in some
embodiments user-specific updates such as described below with
regard to element 540 may be updated for users at other times
(e.g., when an indication of item selection is received that is
subsequent to the generation of the item recommendation model) and
need not be performed in response to a recommendation request.
[0049] As indicated by the positive exit from 530, if item
selections by the particular user are subsequent to the generation
of the item recommendation module, then a user-specific update for
the recommendation model may be calculated as indicate at 540. In
various embodiments, a current user parameter vector for the
particular user may be obtained from the item recommendation model,
and item parameter vectors for the subsequently selected items may
also be obtained may be used to calculate the user-specific update
to the item recommendation model. In various embodiments, the
user-specific update to the item recommendation module may not
update other user parameter vectors or item parameter vectors
stored in the item recommendation model. In at least some
embodiments, the user-specific update to the item recommendation
model may be calculated in real time, such that another singular
value decomposition to update the item recommendation model is not
performed (or cannot be performed prior to calculating the
user-specific update). FIG. 6, discussed below, provides further
detail for calculating the user-specific update to the item
recommendation model.
[0050] One or more item recommendations may then be determined
based, at least in part, on the item recommendation model,
including the user-specific update, as indicated at 550. For
example, the user-specific update may be an updated user parameter
vector which may be used to determine different affinity scores
between the particular user and other items. Item recommendations
may then be made based on the determined affinity scores for the
particular user. FIG. 9 provides further detail and discussion of
determining item recommendations below. Item recommendations may
also be determined for a particular user based on the item
recommendation model without including a user specific update, as
indicated at 560. If, as indicated by the negative exit from 530,
no subsequent item selections by the particular user have occurred,
then a user-specific update may not necessary to make item
recommendations for the particular user. Once item recommendations
have been made, a response to the request for item recommendations
may be made with the determined item recommendations, as indicated
at 570. These item recommendations may be directly communicated to
a particular user or received, formatted, and/or re-communicated
via an intermediary component, such as a network-based
interface.
[0051] Updates to the entire item recommendation model may occur
periodically or aperiodically, in some embodiments. For example,
every 24 hours, singular value matrix decomposition may be
performed on the matrix describing current user item selections to
generate an updated user matrix and item matrix. Thus, in various
embodiments, user-specific updates to the item recommendation model
may be overwritten, removed, or made obsolete upon the performance
of another singular decomposition to update the item recommendation
model. However, updates to the item recommendation model may be
performed less frequently for systems implementing real-time
updates to the item recommendation model, as discussed above with
regard to FIGS. 2B and 2C, as user-specific updates may provide
good approximations of complete updates to the item recommendation
model.
[0052] FIG. 6 is a high-level flowchart illustrating methods and
techniques for calculating a user-specific update for an item
recommendation model for a particular user, according to some
embodiments. As indicated at 610, item selection data for a user
may be accessed to obtain item selections for the user, in some
embodiments. Item selection data may be maintained in a persistent
data store or other storage area. The item selections obtained may
be some or all of the item selections for the user (e.g., all item
selections within the last 6 months). Item selections of the user
that occur subsequent to the generation of an item recommendation
model may be identified, as indicated at 620. For example, the item
recommendation model used for generating item recommendations may
be generated at specific point in time. The item selections may
themselves be associated with particular points in time (e.g.,
times at which an indication of the item selection is received). A
timestamp or other indication may, in various embodiments, indicate
the particular point in time associated with a particular item
selection. The times of each of the item selections may then be
compared to the time the item recommendation model was generated
and those item selections that occur later (i.e., more recently or
subsequent to) than the item recommendation model generation time
may be identified as subsequent item selections of the particular
user.
[0053] As indicated at 630, the item recommendation model may then
be accessed to obtain a user parameter vector for the user and item
parameter vectors for the identified subsequent items, in various
embodiments. The user parameter vector may be the row of values in
the user matrix corresponding user number or identifier in the
single matrix from which the item recommendation model is generated
(as discussed above with regard to FIG. 2A). Similarly, item
parameter vectors may be the rows of values in the item matrix
corresponding to the item number or identifier in the single matrix
from which the item recommendation model is generated.
[0054] In some embodiments, item selections in the single matrix
which indicates selections of items between users and items may
correspond to a particular rating value out of multiple rating
values implementing a rating scheme. For instance, a rating value
may be 1, 2, 3, 4, or 5 stars, or a 4.0 scale, or any other rating
scheme or scale for which different rating values may be
determined. As indicated by the positive exit from 640, if the item
selections correspond to a rating scale, then the item parameter
vector may be scaled according to the corresponding item ratings,
as indicated at 650. For example, in some embodiments, the rating
may correspond to particular number or value which may multiplied
with respect to the item parameter vector. If the item selections
do not correspond to item ratings, as indicated by the negative
exit from 640, then the item parameter vectors may not be scaled,
in at least some embodiments.
[0055] As indicated at 660, vector addition may be performed to
combine the user parameter vector and the item parameter vectors to
generate a user-specific update to the item recommendation model
for the user, in some embodiments. The user-specific update may be
an updated user parameter vector which may be used or included in
determining one or more item recommendations, as discussed below
with regard to FIG. 9. In at least some embodiments, the
user-specific update may be calculated in real-time, without (or
faster than) performing another singular value decomposition to
update the item recommendation model, as discussed above with
regard to FIG. 4.
[0056] Users may, in various embodiments, be associated or linked
to other users. For example, multiple users may be registered under
a common customer account, which may be useful to consolidate
payment information, user logistic information (e.g., shipping
address, digital or computing devices configured to receive items,
etc.), or other information involving the selection of items. Such
commonalities may be useful when determining item recommendations
for a particular user by accounting for the affinities of other
users in the customer account. Other links or associations between
users may also be implemented or determined. For example, users may
establish relationships (e.g., two users may play together/against
one another in a gaming application or multiple users may send
various communications to one another via a social media service).
Users may belong to a particular voluntary grouping such as a
particular item forum, fan group, hobby or common interest
organization, club, or other association that may indicate common
interests. In some embodiments, a user profile, such as may be a
part of a customer account for a network-based enterprise, may
allow for descriptive information about a particular user to be
entered and/or stored, and may be used to identify other users that
may be considered linked or associated with a particular user. Two
user profiles may, for instance, include the same key words (e.g.,
"science fiction"). FIG. 7 is high-level flowchart illustrating
methods and techniques for calculating a user-specific update for a
particular user linked to other users, according to some
embodiments.
[0057] As indicated at 710, users may be identified as linked to a
particular user for a user specific-update to an item
recommendation model for the particular user, in various
embodiments. In some embodiments, other users may be explicitly
linked or associated with the user, and stored in mapping
information or other metadata describing associations with the
particular user. In some embodiments, other users may be
intelligently selected based on common features with a particular
user (e.g., prior interaction with the user, membership in user
groupings, geographic or demographic similarities). The number of
users that may be associated with a particular user may be limited,
in some embodiments, so that selection or identification of linked
users to the particular user may be determined according to various
different priority schemes.
[0058] As indicated at 720, user parameter vectors for the linked
users and item parameter vectors for items selected by the linked
users occurring after the generation of the item recommendation
model may be obtained. If a weighting scheme is applied for linked
users, as indicated by the positive exit from 730, then vector
addition may be performed for individual ones and/or each linked
user to combine the user parameter vector for the linked user with
item parameter vectors for which the user subsequently selected
after the generation of the item recommendation model. For
instance, if linked user A selected items bb and cc, and linked
user B selected items dd and ee, then vector addition may be
performed to combine the user parameter vector of A with the item
parameter vectors for items bb and cc together. Likewise, the user
parameter vector for linked user B may be combined with vector
addition to item parameter vectors for items dd and ee.
[0059] As indicated at 760, individual ones of these updated
parameter vectors for the linked users (e.g., the combined
parameter vectors for user A and user B above) may be scaled
according to the weighting scheme (e.g., multiplying the parameter
vector by a particular scale value). For example, the weighting
scheme may assign ranks, priorities, or other values to certain
types of links to a particular user. For instance, if a linked user
is a member of the same customer account as the particular user,
then the weighted value of that linked user may be higher than a
weighted value for a user that is only identified as linked to the
particular user based on similar key words. Weighting factors may
also be determined based on the type of items being considered for
recommendation in some embodiments. When, for example, a linked
user is identified by a similarity for a particular genre of music
and the item recommendation is for music, then the linked user may
be weighted higher than a linked user associated via common
customer account (which may include users of a wide array of
musical tastes).
[0060] After scaling the updated parameter vectors, vector addition
may be performed to combine the scaled user parameter vectors of
the linked users with a user parameter vector for the particular
user and/or item parameter vectors of items subsequently selected
by the particular user, as indicated at 770. Continuing the example
above, the updated parameter vectors for user A and user B that are
scaled above may be combined with a user vector U, as well as item
parameter vectors ff and gg for subsequent item selections by user
U of items ff and gg. This combined parameter vector may then be
used as the user specific update.
[0061] Alternatively, as incited by the negative exit from 730, the
user parameter vector for the particular user and the users linked
to the user may be combined together using vector addition along
with item parameter vectors for items subsequently selected items
by the particular user and/or linked users, as indicated at 740.
Thus, the vectors may be combined without being scaled, in some
embodiments.
[0062] FIG. 8 is a high-level flowchart illustrating methods and
techniques for comparing item recommendations among a particular
user and other users linked to the particular user, according to
some embodiments. As indicated at 810 users linked to a particular
user may be identified. As noted above, various different
associations, interactions, or commonalities with others who may be
linked to user accounts can be evaluated to identify linked users.
In at least some embodiments, social media associations (contacts
or "friends" lists) may be used to identify linked users. As
indicated at 820, user parameter vectors and item parameter vectors
for the linked users may be obtained (for those linked users that
have subsequently selected items since the item recommendation
model was generated). Then, respective item recommendations may be
generated for individual ones of the linked users based on
user-specific updates calculated for the linked users from the
respective user parameter vectors and item parameter vectors
obtained, as indicated at 830. Please note that item
recommendations may be generated for linked users that have not
subsequently selected items after the generation of the item
recommendation model. Item recommendations for these linked users
may be generated using the respectively maintained user parameter
vectors of these linked users. Once the different sets of item
recommendations per user are generated, the item recommendations
may be compared, as indicated at 840, in order to select commonly
recommended items to provide as recommendations for the particular
users. If, for instance, 3 out of 5 linked users had an item
recommended for them, then the particular item may be provided as
an item recommendation (even if it was not generated based on the
particular user's model information alone). Various different
schemes for weighting or indicating commonality between
recommendations, and thus the previous example is not intended to
be limiting.
[0063] As noted above, item recommendations for a particular user
may be determined based on an item recommendation model generated
from a singular value decomposition of a single matrix that
represents item selections between users and items. A user-specific
update for a particular user may be generated in real-time to be
used for making item recommendations. FIG. 9 is a high-level
flowchart illustrating methods and techniques for determining one
or more item recommendations based on an item recommendation model
generated from matrix factorization, according to some
embodiments.
[0064] As indicated at 910, affinity scores may be calculated for a
particular user for items based on a user specific update to an
item recommendation model, in some embodiments. For example, an
updated user parameter vector may be calculated as a user-specific
update to the item recommendation model (as discussed above with
regard to FIGS. 6 and 7). This user parameter vector may then be
used to generate an affinity vis-a-vis a particular item. For
instance, the dot product of user parameter vector U with a
particular item parameter vector V.sub.j may provide the affinity
of user i for item j. Affinity scores may be generated for a subset
or all of the items described in an item recommendation model. For
example, a particular subset or type of item may be an electronic
product, a particular genre of movie, book or music, and/or a
category of application or service. In some embodiments, the higher
an affinity score, the higher a user's affinity for the item may
be.
[0065] Based, at least in part, on the affinity scores, candidate
item recommendations may be identified, as indicated at 920. For
instance, in some embodiments, the item affinity scores may be
ranked, ordered, grouped, or otherwise arranged according to
affinity score. One or more schemes may be applied to identify the
candidate items, such as taking the highest scoring item in
different particular groupings, or, for instance, identifying a
certain number of items with items above a particular affinity
score threshold. Once identified, a selection of item
recommendations may be made from the candidate item recommendations
according to selection criteria, as indicated at 930. Selection
criteria may, for instance, filter out from item recommendations
those items which the particular user has previously selected (or a
user from linked or associated with the particular user has
selected). Selection criteria may use other types of collaborative
or knowledge-based filtering techniques as selection criteria to
perform selection of item recommendations (e.g., removing content
considered inappropriate for a particular user account known to be
associated with customer of a certain age, such as determined by
item description information like a movie or television show
rating).
[0066] The methods described herein may in various embodiments be
implemented by any combination of hardware and software. For
example, in one embodiment, the methods may be implemented by a
computer system (e.g., a computer system as in FIG. 10) that
includes one or more processors executing program instructions
stored on a computer-readable storage medium coupled to the
processors. The program instructions may be configured to implement
the functionality described herein (e.g., the functionality of
recommendation engines, model generation components, data stores
and/or other components that implement the network-based
enterprises, systems, or services described herein). The various
methods as illustrated in the figures and described herein
represent example embodiments of methods. The order of any method
may be changed, and various elements may be added, reordered,
combined, omitted, modified, etc.
[0067] FIG. 10 is a block diagram illustrating a computer system
configured to implement real-time updates to item recommendation
models based on item factorization described herein, according to
various embodiments. For example, computer system 1000 may be
configured to implement a real-time item recommendation engine,
business logic, enterprise interface or a storage system that
stores the item selection data and/or item recommendation model, in
different embodiments. Computer system 1000 may be any of various
types of devices, including, but not limited to, a personal
computer system, desktop computer, laptop or notebook computer,
mainframe computer system, handheld computer, workstation, network
computer, a consumer device, application server, storage device,
telephone, mobile telephone, or in general any type of computing
device.
[0068] Computer system 1000 includes one or more processors 1010
(any of which may include multiple cores, which may be single or
multi-threaded) coupled to a system memory 1020 via an input/output
(I/O) interface 1030. Computer system 1000 further includes a
network interface 1040 coupled to I/O interface 1030. In various
embodiments, computer system 1000 may be a uniprocessor system
including one processor 1010, or a multiprocessor system including
several processors 1010 (e.g., two, four, eight, or another
suitable number). Processors 1010 may be any suitable processors
capable of executing instructions. For example, in various
embodiments, processors 1010 may be general-purpose or embedded
processors implementing any of a variety of instruction set
architectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS
ISAs, or any other suitable ISA. In multiprocessor systems, each of
processors 1010 may commonly, but not necessarily, implement the
same ISA. The computer system 1000 also includes one or more
network communication devices (e.g., network interface 1040) for
communicating with other systems and/or components over a
communications network (e.g. Internet, LAN, etc.). For example, a
client application executing on system 1000 may use network
interface 1040 to communicate with a server application executing
on a single server or on a cluster of servers that implement one or
more of the components of the database systems described herein. In
another example, an instance of a server application executing on
computer system 1000 may use network interface 1040 to communicate
with other instances of the server application (or another server
application) that may be implemented on other computer systems
(e.g., computer systems 1090).
[0069] In the illustrated embodiment, computer system 1000 also
includes one or more persistent storage devices 1060 and/or one or
more I/O devices 1080. In various embodiments, persistent storage
devices 1060 may correspond to disk drives, tape drives, solid
state memory, other mass storage devices, or any other persistent
storage device. Computer system 1000 (or a distributed application
or operating system operating thereon) may store instructions
and/or data in persistent storage devices 1060, as desired, and may
retrieve the stored instruction and/or data as needed. For example,
in some embodiments, computer system 1000 may host a storage system
server node, and persistent storage 1060 may include the SSDs
attached to that server node.
[0070] Computer system 1000 includes one or more system memories
1020 that are configured to store instructions and data accessible
by processor(s) 1010. In various embodiments, system memories 1020
may be implemented using any suitable memory technology, (e.g., one
or more of cache, static random access memory (SRAM), DRAM, RDRAM,
EDO RAM, DDR 10 RAM, synchronous dynamic RAM (SDRAM), Rambus RAM,
EEPROM, non-volatile/Flash-type memory, or any other type of
memory). System memory 1020 may contain program instructions 1025
that are executable by processor(s) 1010 to implement the methods
and techniques described herein. In various embodiments, program
instructions 1025 may be encoded in platform native binary, any
interpreted language such as Java.TM. byte-code, or in any other
language such as C/C++, Java.TM., etc., or in any combination
thereof. For example, in the illustrated embodiment, program
instructions 1025 include program instructions executable to
implement the functionality of a real-time item recommendation
engine (or module or component thereof), one or more computing
systems, servers or nodes implementing a network-based enterprise,
or storage systems that store the item selection data and/or the
item recommendation model, in different embodiments. In some
embodiments, program instructions 1025 may implement multiple
separate clients, server nodes, and/or other components.
[0071] In some embodiments, program instructions 1025 may include
instructions executable to implement an operating system (not
shown), which may be any of various operating systems, such as
UNIX, LINUX, Solaris.TM., MacOS.TM., Windows.TM., etc. Any or all
of program instructions 1025 may be provided as a computer program
product, or software, that may include a non-transitory
computer-readable storage medium having stored thereon
instructions, which may be used to program a computer system (or
other electronic devices) to perform a process according to various
embodiments. A non-transitory computer-readable storage medium may
include any mechanism for storing information in a form (e.g.,
software, processing application) readable by a machine (e.g., a
computer). Generally speaking, a non-transitory computer-accessible
medium may include computer-readable storage media or memory media
such as magnetic or optical media, e.g., disk or DVD/CD-ROM coupled
to computer system 1000 via I/O interface 1030. A non-transitory
computer-readable storage medium may also include any volatile or
non-volatile media such as RAM (e.g. SDRAM, DDR SDRAM, RDRAM, SRAM,
etc.), ROM, etc., that may be included in some embodiments of
computer system 1000 as system memory 1020 or another type of
memory. In other embodiments, program instructions may be
communicated using optical, acoustical or other form of propagated
signal (e.g., carrier waves, infrared signals, digital signals,
etc.) conveyed via a communication medium such as a network and/or
a wireless link, such as may be implemented via network interface
1040.
[0072] In some embodiments, system memory 1020 may include data
store 1045, which may be configured as described herein. For
example, the information described herein as being stored by
persistent storage, such as the item selection data or the item
recommendation model described herein may be stored in data store
1045 or in another portion of system memory 1020 on one or more
nodes, in persistent storage 1060, and/or on one or more remote
storage devices 1070, at different times and in various
embodiments. In general, system memory 1020 (e.g., data store 1045
within system memory 1020), persistent storage 1060, and/or remote
storage 1070 may store data blocks, replicas of data blocks,
metadata associated with data blocks and/or their state, data
storage configuration information, and/or any other information
usable in implementing the methods and techniques described
herein.
[0073] In one embodiment, I/O interface 1030 may be configured to
coordinate I/O traffic between processor 1010, system memory 1020
and any peripheral devices in the system, including through network
interface 1040 or other peripheral interfaces. In some embodiments,
I/O interface 1030 may perform any necessary protocol, timing or
other data transformations to convert data signals from one
component (e.g., system memory 1020) into a format suitable for use
by another component (e.g., processor 1010). In some embodiments,
I/O interface 1030 may include support for devices attached through
various types of peripheral buses, such as a variant of the
Peripheral Component Interconnect (PCI) bus standard or the
Universal Serial Bus (USB) standard, for example. In some
embodiments, the function of I/O interface 1030 may be split into
two or more separate components, such as a north bridge and a south
bridge, for example. Also, in some embodiments, some or all of the
functionality of I/O interface 1030, such as an interface to system
memory 1020, may be incorporated directly into processor 1010.
[0074] Network interface 1040 may be configured to allow data to be
exchanged between computer system 1000 and other devices attached
to a network, such as other computer systems 1090 (which may
implement one or more storage system server nodes, enterprise
system nodes, and/or clients of the network-based enterprise
systems described herein), for example. In addition, network
interface 1040 may be configured to allow communication between
computer system 1000 and various I/O devices 1050 and/or remote
storage 1070. Input/output devices 1050 may, in some embodiments,
include one or more display terminals, keyboards, keypads,
touchpads, scanning devices, voice or optical recognition devices,
or any other devices suitable for entering or retrieving data by
one or more computer systems 1000. Multiple input/output devices
1050 may be present in computer system 1000 or may be distributed
on various nodes of a distributed system that includes computer
system 1000. In some embodiments, similar input/output devices may
be separate from computer system 1000 and may interact with one or
more nodes of a distributed system that includes computer system
1000 through a wired or wireless connection, such as over network
interface 1040. Network interface 1040 may commonly support one or
more wireless networking protocols (e.g., Wi-Fi/IEEE 802.11, or
another wireless networking standard). However, in various
embodiments, network interface 1040 may support communication via
any suitable wired or wireless general data networks, such as other
types of Ethernet networks, for example. Additionally, network
interface 1040 may support communication via
telecommunications/telephony networks such as analog voice networks
or digital fiber communications networks, via storage area networks
such as Fibre Channel SANs, or via any other suitable type of
network and/or protocol. In various embodiments, computer system
1000 may include more, fewer, or different components than those
illustrated in FIG. 10 (e.g., displays, video cards, audio cards,
peripheral devices, other network interfaces such as an ATM
interface, an Ethernet interface, a Frame Relay interface,
etc.)
[0075] It is noted that any of the system embodiments described
herein, or any of their components, may be implemented as one or
more network-based services, which may or may not be distributed.
For example, a real-time item recommendation may be implemented by
a network-based enterprise that employs the systems described
herein to clients as network-based services. In some embodiments, a
network-based service may be implemented by a software and/or
hardware system designed to support interoperable
machine-to-machine interaction over a network. A network-based
service may have an interface described in a machine-processable
format, such as the Web Services Description Language (WSDL). Other
systems may interact with the network-based service in a manner
prescribed by the description of the network-based service's
interface. For example, the network-based service may define
various operations that other systems may invoke, and may define a
particular application programming interface (API) to which other
systems may be expected to conform when requesting the various
operations. though
[0076] In various embodiments, a network-based service may be
requested or invoked through the use of a message that includes
parameters and/or data associated with the network-based services
request. Such a message may be formatted according to a particular
markup language such as Extensible Markup Language (XML), and/or
may be encapsulated using a protocol such as Simple Object Access
Protocol (SOAP). To perform a network-based services request, a
network-based services client may assemble a message including the
request and convey the message to an addressable endpoint (e.g., a
Uniform Resource Locator (URL)) corresponding to the network-based
service, using an Internet-based application layer transfer
protocol such as Hypertext Transfer Protocol (HTTP).
[0077] In some embodiments, network-based services may be
implemented using Representational State Transfer ("RESTful")
techniques rather than message-based techniques. For example, a
network-based service implemented according to a RESTful technique
may be invoked through parameters included within an HTTP method
such as PUT, GET, or DELETE, rather than encapsulated within a SOAP
message.
[0078] Although the embodiments above have been described in
considerable detail, numerous variations and modifications may be
made as would become apparent to those skilled in the art once the
above disclosure is fully appreciated. It is intended that the
following claims be interpreted to embrace all such modifications
and changes and, accordingly, the above description to be regarded
in an illustrative rather than a restrictive sense.
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