Computer services for identifying and exposing associations between user communities and items in a catalog

• US 20050203807A1
• Filed: 02/14/2005
• Published: 09/15/2005
• Est. Priority Date: 04/09/1999
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: McAuliffe, Jon, Spiegel, Joel R., Bezos, Jeffrey P.
• Granted Patent: US 7,571,121 B2
• Time in Patent Office: Days
• US Class Current: 705/26
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0613 Third-party assisted
• CPC Class Codes: G06Q 30/0623 Item investigation
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 30/0633 Lists, e.g. purchase orders, compilation or processing
• CPC Class Codes: G06Q 30/0641 Shopping interfaces
• CPC Class Codes: G06Q 30/0645 Rental transactions; Leasing transactions
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A computer-implemented service associated with an electronic catalog analyzes purchase histories of users, and/or other types of activity data reflective of user affinities for specific items, to identify items that are significantly more popular in specific user communities than in a general user population. The communities may, for example, include email-based communities (e.g., all users with email addresses associated with a particular company), shipping address based communities (e.g., all users with shipping addresses in Seattle), and/or communities based on other types of user attributes. In one embodiment, a user of the service can select a particular community, such as by selecting the name of a corresponding organization or geographic region, to view a list of items having relatively high popularity levels therein. The results of the analysis may additionally or alternatively be used to affirmatively notify users of associations between particular items and communities, and/or to recommend items to users.

Claims

1. A computer-implemented method of mining data associated with a computer system that provides online access to an electronic catalog of items, the method comprising:

programmatically analyzing item selections of both members and non-members of a user community that represents a subset of a general user population to identify at least one item that is significantly more popular in the user community than in the general user population; and

in response to identifying the at least one item, electronically exposing to at least one user of the electronic catalog an association between the user community and the at least one item to users of the computer system.

2. The method of claim 1, wherein programmatically analyzing the item selections comprises comparing item selection activity levels associated with the user community to corresponding item selection activity levels associated with the general user population.

3. The method of claim 1, wherein programmatically analyzing the item selections comprises using a bootstrap method to statistically evaluate whether a set of items associated with the user community is a result of group preferences that differ from preferences of the general user population.

4. The method of claim 1, wherein programmatically analyzing the item selections comprises using a statistical resampling algorithm to evaluate the item selections.

5. The method of claim 1, wherein the item selections comprise user selections of items to purchase.

6. The method of claim 1, wherein the item selections comprise user selections of items to rent.

7. The method of claim 1, wherein the item selections comprise at least one of the following:

(a) items selected to add to a shopping cart, (b) items selected for viewing in the electronic catalog, (c) items selected to rate.

8. The method of claim 1, wherein the user community is an email-address-based user community.

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

10. The method of claim 1, wherein the user community is a shipping-address-based user community.

11. The method of claim 1, wherein the user community consists of users whose shipping addresses fall within a selected geographic region.

12. The method of claim 1, wherein electronically exposing the association comprises responding to user selection of the user community by outputting a web page that identifies the at least one item.

13. A computer memory having stored therein a web page generated according to the method of claim 12.

14. The method of claim 1, wherein electronically exposing the association comprises notifying a user by email that the at least one item is popular in the user community.

15. A computer system, comprising:

an electronic data repository that stores user activity data associated with each of a plurality of users, said user activity data reflecting user affinities for particular items represented in an electronic catalog;

a first component that analyzes the user activity data of the plurality of users, in conjunction with data that associates particular users with particular user communities, to identify, for each of a plurality of said user communities, a respective set of items that are significantly more popular in the respective user community than in a general user population; and

a second component that provides electronic user access to information that associates the sets of items with the corresponding user communities to assist users in selecting items from the electronic catalog.

16. The computer system of claim 15, wherein the first component identifies a set of items that are significantly more popular in a given user community, at least in part, by comparing item activity levels associated with the given user community to corresponding item activity levels associated with the general user population.

17. The computer system of claim 15, wherein the first component uses a bootstrap method to evaluate whether a set of items associated with a particular user community is a result of group preferences that differ from preferences of the general user population.

18. The computer system of claim 15, wherein the user activity data analyzed by the first component identifies items purchased by specific users.

19. The computer system of claim 15, wherein the user activity data analyzed by the first component identifies items rented by specific users.

20. The computer system of claim 15, wherein the user activity data analyzed by the first component is reflective of item ratings submitted by users.

21. The computer system of claim 15, wherein the user activity data analyzed by the first component reflects click-though events to item detail pages of the electronic catalog.

22. The computer system of claim 15, wherein the user communities include email-address-based user communities.

23. The computer system of claim 15, wherein the user communities include communities associated with particular organizations, and wherein the first component treats a user as being affiliated with one of said organizations if an email address of the user contains a string associated with the organization.

24. The computer system of claim 15, wherein the user communities include shipping-address-based user communities.

25. The computer system of claim 15, wherein the user communities include explicit-membership communities that users explicitly join via a user interface of the computer system.

26. The computer system of claim 15, wherein the second executable component is an interactive component that is responsive to user selection of a particular user community by outputting a list of the set of items corresponding to the particular user community.

27. The computer system of claim 15, wherein the second executable component is a notification component that generates email-based notifications of items that are popular in particular user communities.

28. The computer system of claim 15, wherein the second executable component uses information about the sets of items identified by the first component, in combination with data that associates a target user with a particular user community, to select items to recommend to the target user.

29. The computer system of claim 15, wherein the items include physical products that are shipped to users.

30. The computer system of claim 15, wherein the items include movie titles rented by users.

31. A computer-implemented data mining method, comprising:

providing an electronic data repository that contains user activity data associated with each of a plurality of users of an online system that provides access to an electronic catalog, said user activity data reflecting user-generated events associated with particular items represented in the electronic catalog;

programmatically identifying, among the plurality of users, a subset of users whose email addresses are associated with a particular organization; and

programmatically analyzing the user activity data associated with the plurality of users, including the subset of users, to identify a set of items that have experienced significantly higher levels of user activity among the subset of users than among the plurality of users.

32. The method of claim 31, further comprising electronically exposing to users of the electronic catalog an association between the organization and the set of items.

33. The method of claim 32, wherein electronically exposing the association comprises providing access, via a web site that hosts the electronic catalog, to a web page that lists the set of items and a name of the organization.

34. The method of claim 32, wherein electronically exposing the association comprises transmitting, to an email address of a user, a notification message that associates the organization with one or more items in said set.

35. The method of claim 31, further comprising electronically recommending items in said set to at least some of the users in said subset of users.

36. The method of claim 31, wherein programmatically analyzing the user activity data comprises comparing item popularity levels associated with the subset of users to corresponding item popularity levels associated with the plurality of users.

37. The method of claim 31, wherein the user-generated events comprise item purchase events.

38. The method of claim 31, wherein the user-generated events comprise item rental events.

39. The method of claim 31, wherein the user-generated events include at least one of the following:

(a) user submissions of item ratings, (b) shopping cart add events, (c) click-through events to item detail pages.

40. The computer system of claim 31, wherein the items include physical products that are shipped to users.

41. The computer system of claim 31, wherein the items include movie titles rented by users.