System and method for exposing popular categories of browse tree

• US 6,489,968 B1
• Filed: 11/18/1999
• Issued: 12/03/2002
• Est. Priority Date: 11/18/1999
• Status: Expired due to Term

• Current Assignee: A9.com Incorporated (Amazon.com, Inc.)
• Original Assignee: Amazon.com, Inc.
• Inventors: Ortega, Ruben E., Spiegel, Joel R., Bortscheller, Lauri E.
• Primary Examiner(s): Robinson, Greta L.
• Assistant Examiner(s): Black, Linh
• Application Number:
  US09/442,677
• Time in Patent Office: 1,111 Days
• Field of Search: 707/100-104, 707/500-526, 707/1-10, 345/738-739, 345/811-812, 345/825, 345/594, 345/700, 345/730, 345/733, 345/737, 345/744, 345/713, 705/26-27
• US Class Current: 715/713
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0623 Item investigation
• CPC Class Codes: Y10S 707/99931 Database or file accessing
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access
• CPC Class Codes: Y10S 707/99937 Sorting
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing

Abstract

A computer-implemented process identifies popular nodes (items and/or item categories) within a browse tree or other hierarchial browse structure based on historical actions of online users, and calls such nodes to the attention of users during navigation of the browse tree. The system and method are particularly useful for assisting users in locating popular products and/or product categories within a catalog on an online merchant, but may be used with browse structures for navigating other types of content. Node popularity levels are determined periodically (e.g. once per day) based on recent user activity data that reflects users'"'"' affinities for specific nodes. Popular nodes are called to the attention of users, preferably by automatically “elevating” such nodes for display within the browse tree. The node elevation process may also be used to elevate nodes that are predicted to be of interest to a user, regardless of node popularity levels.

Claims

1. A computer-implemented method for facilitating identification of popular categories of a browse tree that contains multiple levels of item categories, comprising:

monitoring at least one type of user activity that is indicative of user affinities for particular categories of the browse tree;

determining popularity levels of categories using data collected during the step of monitoring; and

based at least upon the popularity levels, elevating popular categories along parent-child paths of the browse tree for display;

wherein the method enables a user to view popular categories that fall multiple levels below a selected parent category without the need to navigate to the popular categories within the browse tree.

2. The method of claim 1, wherein monitoring at least one type of user activity comprises monitoring selections of categories by users during navigation of the browse tree.

3. The method of claim 1, wherein monitoring at least one type of user activity comprises tracking online purchases of items that fall within categories of the browse tree.

4. The method as in claim 1, wherein determining popularity levels comprises periodically processing activity data collected over a selected period of time to determine current popularity levels, so that subcategories are elevated based on current interests of users.

5. The method of claim 1, wherein the browse tree comprises a directed acyclic graph.

6. The method of claim 1, wherein elevating comprises selecting items to be elevated on a user-specific basis.

7. The method of claim 6, wherein determining popularity levels comprises generating user-specific and non-user-specific popularity scores for each of a plurality of categories, and elevating comprises using the user-specific and non-user-specific scores to select categories to be elevated on a user-specific basis.

8. The method of claim 1, wherein elevating comprises featuring a subcategory on a Web page that corresponds to a non-immediate parent of the subcategory.

9. The method of claim 1, wherein only leaf categories of the browse tree are elevated.

22. A computer program which, when executed by a computer system, is capable of performing the method of claim 1.

10. A computer-implemented method for facilitating identification of popular categories of a browse tree that contains multiple levels of item categories, comprising:

monitoring at least one type of user activity that is indicative of user affinities for particular categories of the browse tree to determine popularity levels of specific categories; and

based at least upon the popularity levels, elevating popular categories along parent-child paths of the browse tree for display within the browse tree;

wherein the method automatically exposes popular categories that fall multiple levels below a selected parent category during navigation of the browse tree.

11. The method of claim 10, wherein monitoring at least one type of user activity comprises monitoring selections of categories by users during navigation of the browse tree.

12. The method of claim 10, wherein monitoring at least one type of user activity comprises tracking online purchases of items that fall within categories of the browse tree.

13. The method as in claim 10, wherein monitoring comprises periodically processing activity data collected over a selected period of time to determine current popularity levels, so that categories are elevated based on current interests of users.

14. The method of claim 10, wherein the browse tree comprises a directed acyclic graph.

15. The method of claim 10, wherein elevating comprises selecting categories to be elevated on a user-specific basis.

16. The method of claim 15, wherein monitoring comprises generating user-specific and non-user-specific popularity scores for each of a plurality of categories, and the elevating comprises using the user-specific and non-user-specific scores to select categories to be elevated on a user-specific basis.

17. The method of claim 10, wherein elevating comprises featuring a subcategory on a Web page that corresponds to a non-immediate parent of the subcategory.

23. A computer program which, when executed by a computer system, is capable of performing the method of claim 10.

18. A system for facilitating identification of popular categories during browsing of a database of items, the system comprising:

a hierarchical browse structure stored within a computer memory for allowing users to locate items within the database according to predefined categories and subcategories of items;

a first process which tracks at least one type of user activity that is indicative of user affinities for particular categories of the browse structure to generate category popularity data; and

a second process which uses at least the category popularity data to elevate popular categories for display within the hierarchical browse structure to expose the popular categories to users during viewing of corresponding, non-immediate parent categories.

19. The system of claim 18, wherein the hierarchical browse structure comprises a directed acyclic graph.

20. The system of claim 18, wherein the second process uses user-specific category affinity data to elevate categories on a user-specific basis, so that categories are elevated differently for different users of the browse structure.

21. The system of claim 18, wherein the first process periodically uses activity data collected over a selected period of time to generate category popularity data that reflects current interests of users.

24. A method of assisting users in browsing a hierarchical browse structure in which items are arranged by category, said hierarchical browse structure including multiple levels of categories, and being browsable by users to locate specific items within a database of items, the method comprising:

monitoring user actions that are reflective of user affinities for particular categories of the hierarchical browse structure, said user actions including actions of a plurality of users;

analyzing user activity data resulting from said monitoring of user actions to generate category popularity data reflective of current popularity levels of specific categories of the hierarchical browse structure among said plurality of users; and

when a user accesses a first category of said hierarchical browse structure, selecting a second category to present to the user based at least in part on the category popularity data, the second category selected from a set of categories that fall below the first category within the hierarchical browse structure.

25. The method of claim 24, wherein the second category is selected from a set of categories that are non-immediate children of the first category within the hierarchical browse structure.

26. The method of claim 25, wherein said set of categories consists of leaf categories that are non-immediate children of the first category.

27. The method of claim 24, wherein the category popularity data is based at least in-part on category selection actions of users during navigation of the hierarchical browse structure.

28. The method of claim 24, wherein the category popularity data is based at least in-part on user actions performed with respect to the items.

29. The method of claim 24, wherein the second category is selected to present to the user based further upon a profile of said user.

30. The method of claim 24, wherein analyzing the user activity data comprises periodically analyzing a most recent set of the user activity data.

31. The method of claim 24, wherein the hierarchical browse structure is a pure tree.

32. The method of claim 24, wherein the hierarchical browse structure is a directed acyclic graph.

33. A computer program which, when executed by a computer system, is capable of performing the method of claim 24.

34. A web page generated according to the method of claim 24, said web page including representations of the first and second categories.

35. The method of claim 24, further comprising, when the user accesses the first category, selecting an item to present to the user from a set of items falling within subcategories of the first category, wherein the item is selected based on item popularity data.