Computer processes for selecting nodes to call to attention of a user during browsing of a hierarchical browse structure

• US 6,606,619 B2
• Filed: 07/29/2002
• Issued: 08/12/2003
• 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: Spiegel, Joel R., Bortscheller, Lauri E., Ortega, Ruben E.
• Primary Examiner(s): Robinson, Greta
• Assistant Examiner(s): Black, Linh
• Application Number:
  US10/208,337
• Publication Number:
  US 20030050916A1
• Time in Patent Office: 379 Days
• Field of Search: 707/1-10, 707/100-104
• US Class Current: 1/1
• 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 specific nodes within a browse tree or other hierarchical browse structure based on historical actions of online users, and calls such nodes to the attention of users during navigation of the browse structure. The system and method are particularly useful for assisting users in locating popular products and/or product categories within a catalog of an online merchant, but may be used in connection with browse structures used to locate other types of items. In one embodiment, node popularity levels are determined periodically (e.g., once per day) based on user activity data that represents users'"'"' affinities for such nodes (items and/or item categories). 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 particular user.

Claims

1. A system for facilitating browsing of hierarchical browse structure that comprises a plurality of nodes, wherein leaf nodes represent items and non-leaf nodes represent item categories, the system comprising:

a tracking component that monitors and records user actions of a plurality of users to generate user activity data, said user actions including actions reflective of user affinities for specific nodes of the hierarchical browse structure;

an analysis component that analyzes the user activity data and generates scores for specific nodes of the hierarchical browse structure; and

a node elevation component that uses the scores to select specific nodes of the hierarchical browse structure to elevate for display to users;

wherein, by elevating a selected node for display to a user, the node elevation component enables the user to view the selected node during browsing of the hierarchical browse structure without drilling down within the hierarchical browse structure to a level of the selected node.

2. The system as in claim 1, wherein the analysis component periodically regenerates the scores such that the scores reflect most recently recorded sets of user activity data, whereby different nodes are selected for elevation in different time periods according to changes in user browsing patterns.

3. The system as in claim 1, wherein the node elevation component elevates the selected nodes along parent-child paths of the hierarchical browse structure.

4. The system as in claim 1, wherein the scores generated by the analysis component reflect current popularity levels of the nodes among the plurality of users.

5. The system as in claim 1, wherein the analysis component generates community-specific sets of said scores for each of a plurality of user communities, and wherein the node elevation component selects nodes to elevate for a given user based on a set of scores corresponding to a user community of which the given user is a member.

6. The system as in claim 1, wherein the analysis component generates user-specific scores, and the node elevation component uses the user-specific scores to personalize selections of nodes to elevate for users.

7. The system as in claim 1, wherein the node elevation component selects leaf nodes to elevate for display in conjunction with a non-leaf category node at least in-part by identifying a set of leaf nodes that fall below the non-leaf category node;

and selecting a subset of said set of leaf nodes to elevate.

8. The system as in claim 1, wherein the nodes selected for elevation by the node elevation component include leaf nodes that represent respective items.

9. The system as in claim 1, wherein the nodes selected for elevation by the node elevation component include nodes that represent respective item subcategories.

10. The system as in claim 1, wherein the hierarchical browse structure is a directed acyclic graph structure.

11. The system as in claim 1, wherein the node elevation component selects nodes to elevate recursively on a node-by-node basis.

12. A computer-implemented method for facilitating browsing of a hierarchical browse structure in which each node represents an item or an item category, the method comprising:

monitoring at least one type of user activity that is indicative of user affinities for particular nodes of the hierarchical browse structure;

generating scores using data collected during said monitoring, the scores reflecting at least one of (a) popularity levels of particular nodes, and (b) predicted user affinities for particular nodes; and

based at least in-part upon on the scores, elevating selected nodes along parent-child paths of the hierarchical browse structure for display;

wherein, by elevating a node, the method enables a user to view the node during browsing of the hierarchical browse structure without having to navigate down to a level of the node within the hierarchical browse structure.

13. The method as in claim 12, wherein generating scores comprises periodically processing user activity data collected over a selected period of time to generate scores reflective of current node popularity levels.

14. The method of claim 12, wherein the browse structure is a directed acyclic graph structure.

15. The method as in claim 12, wherein monitoring at least one type of user activity comprises tracking online purchases of items.

16. The method as in claim 12, wherein generating scores comprises applying a recommendation algorithm to activity data collected for a particular user to generate user-specific scores.

17. The method as in claim 12, wherein elevating selected nodes comprises selecting nodes to elevate on a user-specific basis.

18. The method as in claim 12, wherein elevating selected nodes comprises:

identifying a set of leaf nodes that fall below a current node being accessed by a user; and

selecting a subset of said set of leaf nodes to elevate for display in conjunction with the current node.

19. The method as in claim 12, wherein the method comprises generating sets of said scores separately for each of a plurality of user communities, and using a set of scores associated with each community to elevate nodes for members of that community, whereby nodes are elevated differently for different users.

20. The method of claim 12, wherein the selected nodes elevated for display include nodes that represent respective items.

21. The method of claim 20, wherein at least some of the items are auctions.

22. The method of claim 12, wherein the selected nodes elevated for display include nodes that represent respective item subcategories.

23. A computer system that embodies the method of claim 12.

24. A web page generated in accordance with the method of claim 12, the web page including a representation of a first node of the hierarchical browse structure, and further including a representation of a second node that has been elevated for display in conjunction with the first node.

25. A system for facilitating browsing of an electronic catalog of items, the system comprising:

a database which contains information about a plurality of items;

a hierarchical browse structure represented within a server system that provides user access to the database, wherein items from the database are arranged within the hierarchical browse structure according to a hierarchy of item categories, and wherein the hierarchical browse structure is interactively browsable by users to locate items according to the hierarchy of item categories;

a first process which tracks at least one type of user activity that is reflective of user affinities for particular items in the hierarchical browse structure to generate item popularity data; and

a second process which uses at least the item popularity data to elevate items for display to expose popular items to users during viewing of non-leaf categories of the hierarchical browse structure.

26. The system of claim 25, wherein the hierarchical browse structure is a directed acyclic graph.

27. The system of claim 25, wherein the first process additionally tracks at least one type of user activity that is reflective of user affinities for particular categories of items to generate category popularity data, and the second process further uses the category popularity data to select items to elevate.

28. The system of claim 27, wherein the second process further uses the category popularity data to select leaf categories to elevate for display.

29. The system of claim 25, wherein the second process uses user-specific data to elevate items on a user-specific basis, so that items are elevated differently for different users of the hierarchical browse structure.

30. The system of claim 25, wherein the first process periodically uses activity data collected over a selected period of time to generate item popularity data that reflects current interests of users.

31. The system of claim 25, wherein the second process elevates the items for display along parent-child paths of the hierarchical browse structure.

32. 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 items in 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 item popularity data reflective of current popularity levels of specific items of the hierarchical browse structure among said plurality of users; and

when a user accesses a non-leaf category of said hierarchical browse structure, selecting, from a set of items that fall below the non-leaf category within the hierarchical browse structure, at least one item to present to the user based at least in-part on the item popularity data.

33. The method of claim 32, wherein said set of items consists of items that are non-immediate children of the non-leaf category.

34. The method of claim 32, wherein the item popularity data is based at least in-part on item selection actions of users during navigation of the hierarchical browse structure.

35. The method of claim 32, wherein the at least one item is selected to present to the user based further upon a profile of said user.

36. The method of claim 32, wherein analyzing the user activity data comprises periodically analyzing a most recent set of the user activity data.

37. The method of claim 32, wherein the hierarchical browse structure is a pure tree.

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

39. A computer program which, when executed by a computer system, performs the method of claim 32.

40. A web page generated according to the method of claim 32, said web page including representations of the non-leaf category and the at least one item.

41. The method of claim 32, wherein selecting at least one item to present to the user comprises recursively selecting items to elevate within each of a plurality of categories falling below said non-leaf category.

42. A method of personalizing the display of a hierarchical browse structure in which items are arranged within multiple levels of item categories, and in which items and item categories are represented as respective nodes of the hierarchical browse structure, the method comprising:

monitoring online actions of the user over time to collect personal user activity data;

analyzing the personal user activity data to generate, for each of a plurality of nodes of the hierarchical browse structure, a user-specific score reflective of the user'"'"'s predicted affinity for that node, to thereby generate a plurality of user-specific scores; and

using the user-specific scores to select, from a set of nodes falling below a first node within the hierarchical browse structure, a second node to elevate for display such that the second node is exposed to the user when the user accesses the first node;

wherein, by elevating the second node, the second node is exposed to the user without the user having to drill down within the hierarchical browse structure below the first node to a level of the second node.

43. The method of claim 42, wherein the second node represents a leaf category.

44. The method of claim 42, wherein the second node is a leaf node that represents a single item.

45. The method of claim 42, wherein the first node resides multiple levels above the second node in the hierarchical browse structure.

46. The method of claim 42, wherein the set of nodes consists of nodes falling multiple levels below the first node within the hierarchical browse structure.

47. The method of claim 42, wherein the second node is selected based further upon node popularity data reflective of actions of a plurality of users.

48. The method of claim 42, wherein the hierarchical browse structure is a directed acyclic graph.

49. The method of claim 42, wherein analyzing the personal user activity data comprises applying a personal recommendations algorithm to the personal user activity data.

50. A computer system that embodies the method of claim 42.