Distributed system for facilitating exchange of user information and opinion using automated collaborative filtering

• US 6,112,186 A
• Filed: 03/31/1997
• Issued: 08/29/2000
• Est. Priority Date: 06/30/1995
• Status: Expired due to Term

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Bergh, Christopher P., Ritter, David Henry, Metral, Max E., Sullivan, James J., Sheena, Jonathan Ari
• Primary Examiner(s): Stamber, Eric W.
• Assistant Examiner(s): Irshadullah, M.
• Application Number:
  US08/828,631
• Time in Patent Office: 1,247 Days
• Field of Search: 705/10, 705/14, 705/7, 364/554, 364/419, 380/49, 395/704
• US Class Current: 705/7.32
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9536 Search customisation based on social or collaborative filtering
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0237 at kiosk
• CPC Class Codes: G06Q 30/0239 Online discounts or incentives
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data

Abstract

A system for facilitating exchange of user information and opinion using automated collaborative filtering includes memory elements for storing item profiles and user profiles. The data contained in those profiles is used to calculate a number of similarity factors representing how closely the preferences of one user correlate with another. The similarity factors are evaluated to select a set of neighboring users for each user which represents the set of users which most closely correlate with a particular user. The system assigns a weight to each one of the neighboring users. The system uses the ratings given to items by those neighboring users to recommend an item to a user. The system may be distributed, i.e. the system may include a number of nodes connected to a central server. The central server includes a memory element for storing user profile data and the nodes may be the type of system described above.

Claims

1. Client-server based apparatus for recommending an item to one of a plurality of users situated at a client computer, the client computer connected to a server computer, wherein the item has not yet been rated by the one user, the apparatus comprising:

(A) the server computer having a first memory associated therewith, wherein the server;

(A1) stores a user profile, in the first memory, for each of a plurality of users, wherein the user profile comprises a separate rating value, supplied by a particular one of the users, for each corresponding one of a plurality of items, said items including the item non-rated by the user;

(A2) stores an item profile, in the first memory, for each of the rated items, comprises a separate rating value, for a particular one of the items, provided by each one of the plurality of the users, wherein the user profile and the item profile are distinct from each other; and

(A3) in response to a request issued by the client computer, accesses rating information from the user and item profiles stored in the first memory and provides the rating information to the client computer; and

(B) the client computer comprising;

(B1) a processor; and

(B2) a second memory, connected to the processor, for storing computer executable instructions therein; and

(B3) wherein the processor, in response to the executable instructions;

(B3a) issues, in response to interaction with the one user, the request to the server computer for the rating information;

(B3b) calculates, for each one of the plurality of users and in response to the rating information received from the server computer, a plurality of similarity factors, between said each one user and at least one other one of the users, for each of said items, including said non-rated item;

(B3c) selects, in response to the plurality of similarity factors and for each one of the plurality of users, a plurality of neighboring ones of the users, such that each of the neighboring ones of the users has an associated similarity factor which is greater than a first predefined threshold value or, if a confidence factor is associated with the associated similarity factor, both the associated similarity factor is less the first predefined threshold and the confidence factor exceeds a second predefined threshold value;

(B3d) assigns a corresponding weight to each of the neighboring users so as to define a plurality of weights; and

(B3e) recommends at least one of a plurality of the items to said one user in response to the plurality of weights and ratings given to the non-rated item by the neighboring ones of the users.

2. The apparatus in claim 1 wherein the processor, in response to the executable instructions:

obtains additional rating information for at least one item from a given one of the users; and

supplies the additional rating information to the server computer; and

the server computer updates, in response to the additional rating information, the user and item profiles stored in the first memory.

3. The apparatus in claim 2 wherein profile data, forming the user and item profiles stored in the first memory, is organized into a plurality of profile sections and the request specifies a particular one of the profile sections for which rating information is to be provided by the server computer, the one section being associated with a class of said items through which a recommendation could be obtained through the client computer to the one user.

4. The apparatus in claim 3 wherein the client computer is situated at a kiosk.

5. The apparatus in claim 3 wherein the server computer further comprises means for permitting a plurality of users, each stationed at a different one of a plurality of client computers, to share information amongst themselves related to the items.

6. The apparatus in claim 3 wherein the user profile comprises either a set of user n-tuples or a first set of pointers to storage locations in the memory at which user entries are stored, wherein each of the user n-tuples comprises a separate rating value, supplied by a particular one of the users, for each corresponding one of a plurality of items, said items including the item non-rated by the user and each of the user entries stores a separate rating for an associated one of the users for a corresponding one of the items.

7. The apparatus in claim 6 wherein the item profile comprises either a set of item n-tuples or a second set of pointers to storage locations in the memory at which item entries are stored, wherein each of the item n-tuples comprises a separate rating value, for a particular one of the items, provided by each one of the plurality of the users and each of the item entries stores a rating for an associated one of the items by a corresponding one of the users.

8. The apparatus in claim 7 wherein the calculating step further comprises the steps of:

receiving a rating from the given one of the users for a given one of the plurality of items so as to define a received rating;

updating the user profile associated with the given one user by either writing the received rating into an appropriate one of the user n-tuples or writing the received rating into an appropriate one of the user entries;

updating the item profile associated with the given one item by writing the received rating into an appropriate one of the item n-tuples or writing the received rating into an appropriate one of the item entries; and

calculating, for the given one user, a plurality of said similarity factors, wherein each of the similarity factors for said given one user represents similarity between said given one user and another one of the users with respect to the items.

9. The apparatus in claim 3 wherein the item profile comprises either a set of item n-tuples or a second set of pointers to storage locations in the memory at which item entries are stored, wherein each of the item n-tuples comprises a separate rating value, for a particular one of the items, provided by each one of the plurality of the users and each of the item entries stores a rating for an associated one of the items by a corresponding one of the users.

10. The apparatus in claim 9 wherein the user profile comprises either a set of user n-tuples or a first set of pointers to storage locations in the memory at which user entries are stored, wherein each of the user n-tuples comprises a separate rating value, supplied by a particular one of the users, for each corresponding one of a plurality of items, said items including the item non-rated by the user and each of the user entries stores a separate rating for an associated one of the users for a corresponding one of the items.

11. The apparatus in claim 10 wherein the calculating step further comprises the steps of:

receiving a rating from the given one of the users for a given one of the plurality of items so as to define a received rating;

updating the user profile associated with the given one user by either writing the received rating into an appropriate one of the user n-tuples or writing the received rating into an appropriate one of the user entries;

updating the item profile associated with the given one item by writing the received rating into an appropriate one of the item n-tuples or writing the received rating into an appropriate one of the item entries; and

calculating, for the given one user, a plurality of said similarity factors, wherein each of the similarity factors for said given one user represents similarity between said given one user and another one of the users with respect to the items.