Mining of user event data to identify users with common interest

• US 8,554,723 B2
• Filed: 07/12/2012
• Issued: 10/08/2013
• Est. Priority Date: 03/30/2005
• Status: Expired due to Fees

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: Spiegel, Joel R.
• Primary Examiner(s): Beausoliel, Jr., Robert
• Assistant Examiner(s): Arjomandi, Noosha
• Application Number:
  US13/548,047
• Publication Number:
  US 20120278317A1
• Time in Patent Office: 453 Days
• Field of Search: 707/609, 707/706, 707/802, 705/7.29, 705/10
• US Class Current: 707/609
• CPC Class Codes: G06F 16/24 Querying
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/08 Auctions
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 12/1813 for computer conferences, e.g. chat rooms instant messaging H04L51/04; protocols for multimedia communication H04L65/1101; arrangements for multi-party communication H04L65/403; telephonic conference arrangements H04M3/56; television conference systems H04N7/15
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 67/306 User profiles

Abstract

A computer-implemented matching service matches users to other users, and/or to user communities, based at least in part on a computer analysis of event data reflective of user behaviors. The event data may, for example, evidence user affinities for particular items represented in an electronic catalog, such as book titles, music titles, movie titles, and/or other types of items that tend to reflect the traits of users. Event data reflective of other types of user actions, such as item-detail-page viewing events, browse node visits, search query submissions, and/or web browsing patterns may additionally or alternatively be considered. By taking such event data into consideration, the matching service reduces the burden on users to explicitly supply personal profile information, and reduces poor results caused by exaggerations and other inaccuracies in such profile information.

Claims

1. A computer-implemented method of matching users to other users, the method comprising:

storing, in computer storage, event data comprising order data reflective of items ordered from an electronic catalog by each of a plurality of users;

programmatically generating a score that reflects a degree to which item preferences of a first user of said plurality of users are similar to item preferences of a second user of said plurality of users, said score taking into consideration a first plurality of items ordered by the first user, a second plurality of items ordered by the second user, and a type of one or more of the items ordered in common between the first and second users, said score further taking into consideration at least one additional type of event data reflective of user affinities for items represented in the electronic catalog, wherein generating the score further comprises accessing item similarity data to determine whether an item ordered by the first user is similar to an item ordered by the second user; and

based at least in part on the score, programmatically determining whether to recommend the second user to the first user.

2. The method of claim 1, wherein the score reflects a degree to which the first plurality of items and the second plurality of items are related.

3. The method of claim 1, wherein generating the score further comprises according greater weight to ordered items that are deemed to be reflective of user traits.

4. The method of claim 1, wherein generating the score further comprises according greater weight to items that are ordered by both users.

5. The method of claim 1, wherein the at least one additional type of event data includes data descriptive of at least one of the following events:

(a) an addition of an item to an electronic shopping cart, (b) an addition of an item to an electronic rental queue, (c) a selection of an item for viewing, (d) a submission of a rating of an item, (e) a selection of an item category in a browse structure, (f) a submission of a search query to search the electronic catalog.

6. The method of claim 1, wherein the score is further reflective of a degree to which respective search histories of the first and second users are similar.

7. The method of claim 1, wherein the method is performed in response to a search request submitted by the first user.

8. The method of claim 1, wherein the order data represents at least one of the following:

(a) purchase orders of users, (b) rental orders of users.

9. The method of claim 1, further comprising, in response to a determination that the second user is to be recommended to the first user, electronically providing to the first user information sufficient for the first user to contact the second user.

10. The method of claim 9, further comprising electronically providing to the first user information about items ordered by the second user.

11. The method of claim 1, wherein generating the score comprises:

identifying items of a first type ordered in common between the first and second plurality of items;

identifying items of a second, different type ordered in common between the first and second plurality of items; and

weighting each of the identified common items as a function of the respective type of the identified common item.

12. The method of claim 11, wherein the weights associated with the first and second types of items ordered in common are determined based on a prioritization provided by the first user with respect to the first and second types of items.

13. The method of claim 11, wherein the weights associated with the first and second types of items ordered in common are specified by the first user.

14. A system of matching users to other users, the system comprising:

at least one data store for storing event data comprising order data reflective of items ordered from an electronic catalog by each of a plurality of users; and

at least one computing device including a processor in communication with the at least one data store, the at least one computing device operable to;

programmatically generate a score that reflects a degree to which item preferences of a first user of said plurality of users are similar to item preferences of a second user of said plurality of users, said score taking into consideration a first plurality of items ordered by the first user, a second plurality of items ordered by the second user, and a type of one or more of the items ordered in common between the first and second users, said score further taking into consideration at least one additional type of event data reflective of user affinities for items represented in the electronic catalog, wherein generating the score further comprises accessing item similarity data to determine whether an item ordered by the first user is similar to an item ordered by the second user; and

and based at least in part on the score, programmatically determine whether to recommend the second user to the first user.

15. The system of claim 14, wherein the score reflects a degree to which the first plurality of items and the second plurality of items are related.

16. The system of claim 14, wherein generating the score further comprises according greater weight to ordered items that are deemed to be reflective of user traits.

17. The system of claim 14, wherein generating the score further comprises according greater weight to items that are ordered by both users.

18. The system of claim 14, wherein the at least one additional type of event data includes data descriptive of at least one of the following events:

(a) an addition of an item to an electronic shopping cart, (b) an addition of an item to an electronic rental queue, (c) a selection of an item for viewing, (d) a submission of a rating of an item, (e) a selection of an item category in a browse structure, (f) a submission of a search query to search the electronic catalog.

19. The system of claim 14, wherein the score is further reflective of a degree to which respective search histories of the first and second users are similar.

20. The system of claim 14, wherein the method is performed in response to a search request submitted by the first user.

21. The system of claim 14, wherein the order data represents at least one of the following:

(a) purchase orders of users, (b) rental orders of users.

22. The system of claim 14, further comprising, in response to a determination that the second user is to be recommended to the first user, electronically providing to the first user information sufficient for the first user to contact the second user.

23. The system of claim 22, further comprising electronically providing to the first user information about items ordered by the second user.

24. The system of claim 14, wherein generating the score comprises:

identifying items of a first type ordered in common between the first and second plurality of items;

identifying items of a second, different type ordered in common between the first and second plurality of items; and

weighting each of the identified common items as a function of the respective type of the identified common item.

25. The system of claim 24, wherein the weights associated with the first and second types of items ordered in common are determined based on a prioritization provided by the first user with respect to the first and second types of items.

26. The system of claim 24, wherein the weights associated with the first and second types of items ordered in common are specified by the first user.