Mining of user event data to identify users with common interests

• US 9,160,548 B2
• Filed: 11/17/2014
• Issued: 10/13/2015
• Est. Priority Date: 03/30/2005
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: Spiegel, Joel R.
• Primary Examiner(s): Arjomandi, Noosha
• Application Number:
  US14/543,566
• Publication Number:
  US 20150066791A1
• Time in Patent Office: 330 Days
• Field of Search: 707/609
• US Class Current: 1/1
• 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 user communities, the method comprising:

for each of a plurality of users of an electronic catalog, storing event data representing user-generated events that reflect user affinities for particular items represented in the electronic catalog;

storing information about a plurality of user communities, each of which corresponds to a respective subset of the plurality of users and comprises multiple users;

identifying a first plurality of items that characterize item preferences of a first user community of the plurality of user communities;

calculating a degree to which the first plurality of items are related to a set of items in the electronic catalog identified by the first user; and

based at least in part on the degree, determining whether to suggest the first user community to the first user;

wherein the method is performed by one or more computers.

2. The method of claim 1, wherein calculating the degree comprises accessing item similarity data to determine whether the first plurality of items are similar to the set of items identified by the first user.

3. The method of claim 1, wherein the event data identifies at least one of the following:

(a) items purchased by particular users, (b) items rented by particular users, (c) items added to an electronic shopping cart by particular users, (d) items added to a rental queue by particular users, (e) items rated by particular users, (f) items selected for viewing by particular users, (g) search queries submitted by particular users to search the electronic catalog.

4. The method of claim 1, wherein the event data reflects orders placed by users for items represented in the electronic catalog.

5. The method of claim 4, wherein calculating the degree comprises taking into consideration at least one additional type of event data reflective of user affinities for items represented in the electronic catalog.

6. The method of claim 1, wherein the event data identifies books ordered by particular users.

7. The method of claim 1, wherein calculating the degree comprises weighting at least one type of item more heavily than another type of item.

8. The method of claim 1, wherein the first user community is an explicit-membership community that users explicitly join via a user interface associated with the electronic catalog.

9. The method of claim 1, wherein the first user community comprises users having email addresses associated with a particular organization.

10. A system of matching users to user communities, the system comprising:

memory for storing event data representing user-generated events that reflect user affinities for particular items represented in the electronic catalog and for storing information about a plurality of user communities, each of which corresponds to a respective subset of the plurality of users and comprises multiple users; and

at least one computing device including a processor in communication with the memory, the at least one computing device operative to;

identify a first plurality of items that characterize item preferences of a first user community of the plurality of user communities;

calculate a degree to which the first plurality of items are related to a set of items in the electronic catalog identified by the first user; and

based at least in part on the degree, determine whether to suggest the first user community to the first user.

11. The system of claim 10, wherein calculating the degree comprises accessing item similarity data to determine whether the first plurality of items are similar to the set of items identified by the first user.

12. The system of claim 10, wherein the event data identifies at least one of the following:

(a) items purchased by particular users, (b) items rented by particular users, (c) items added to an electronic shopping cart by particular users, (d) items added to a rental queue by particular users, (e) items rated by particular users, (f) items selected for viewing by particular users, (g) search queries submitted by particular users to search the electronic catalog.

13. The system of claim 10, wherein the event data reflects orders placed by users for items represented in the electronic catalog.

14. The system of claim 13, wherein calculating the degree further comprises taking into consideration at least one additional type of event data reflective of user affinities for items represented in the electronic catalog.

15. The system of claim 10, wherein the event data identifies books ordered by particular users.

16. The system of claim 10, wherein calculating the degree comprises weighting at least one type of item more heavily than another type of item.

17. The system of claim 10, wherein the first user community is an explicit-membership community that users explicitly join via a user interface associated with the electronic catalog.

18. The system of claim 10, wherein the first user community comprises users having email addresses associated with a particular organization.

19. A computer-implemented method comprising:

for each of a plurality of users of an electronic catalog, collecting and storing event data representing user-generated events that reflect user affinities for particular items represented in the electronic catalog, said event data collected via a computer network without requiring the users to explicitly provide personal preference information;

analyzing the event data to generate a score that reflects a degree to which behaviors of a first user of said plurality of users are similar to behaviors of a second user of said plurality of users; and

based at least in part on the score, determining whether to output the first user information about the second user;

wherein the method is performed automatically by one or more computers.

20. A system comprising:

memory for storing event data representing user-generated events that reflect user affinities for particular items represented in the electronic catalog, said event data collected via a computer network without requiring the users to explicitly provide personal preference information; and

at least one computing device including a processor in communication with the memory, the at least one computing device operative to;

analyze the event data to generate a score that reflects a degree to which behaviors of a first user of said plurality of users are similar to behaviors of a second user of said plurality of users; and

based at least in part on the score, determining whether to output the first user information about the second user.