System and method for exposing popular nodes within a browse tree

• US 6,466,918 B1
• Filed: 11/18/1999
• Issued: 10/15/2002
• 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): Rice, Kenneth R.
• Assistant Examiner(s): JASMIN, LYNDA C
• Application Number:
  US09/443,013
• Time in Patent Office: 1,062 Days
• Field of Search: 705/14, 705/27, 705/26, 707/102, 707/103
• US Class Current: 705/7.29
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing
• 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 30/0641 Shopping interfaces
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

A computer-implemented system and method are provided for identifying popular nodes within a browse tree or other hierarchical browse structure based on historical actions of online users, and for calling 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 of an online merchant, but may be used in connection with browse structures used to locate other types of items. Node popularity levels are determined periodically (e.g., once per day) based on recent user activity data that represents users'"'"' affinities for such nodes. Such activity data may include, for example, the number of times each item was purchased, and/or the number of times each category was selected for display, within a selected period of time. Popular nodes are called to the attention of users by automatically “elevating” the nodes for display within the browse tree. For example, when a user selects a particular non-leaf category (a category that contains subcategories) for viewing, the most popular items corresponding to the selected category may be displayed (together with the immediate subcategories), allowing the user to view or directly access these items without having to navigate to lower levels of the browse tree (and particularly those associated with leaf categories). Subcategories may be elevated for display in a similar manner. The node elevation process may also be used to elevate items and/or categories that are predicted to be of interest to a user, regardless of popularity. In a preferred embodiment, both popular items are leaf categories are elevated on a user-specific basis using a combination of user-specific and non-user-specific activity data.

Claims

1. A computer-implemented method for facilitating identification of popular items within an electronic catalog of an online store, comprising:

providing a browse tree which contains a plurality of nodes arranged in a plurality of levels, wherein leaf nodes represent items of the catalog and non-leaf nodes represent categories of items, said browse tree being browsable by users to locate items according to item categories and subcategories;

processing at least the purchase history data to generate node popularity data; and

based at least in-part on the node popularity data, automatically elevating selected items for display to expose popular items to users during viewing of corresponding non-leaf categories of the browse tree;

whereby popular items are brought to the attention of users during browsing of non-leaf categories to which such popular items correspond.

2. The method of claim 1, wherein processing at least the purchase history data comprises applying a sliding window to collected purchase history data to periodically generate item popularity data that reflects current interests of users.

3. The method of claim 1, wherein processing at least the purchase history data comprises processing at least one additional type of user activity data that indicates user affinities for particular items.

4. The method of claim 1, wherein the node popularity data includes user-specific node popularity data, and elevating leaf nodes comprises elevating leaf nodes based on the user-specific node popularity data to customize the browse tree on a user-specific basis.

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

6. The method of claim 1, wherein elevating comprises featuring popular items on a Web page that corresponds to a non-leaf category.

7. The method of claim 1, wherein processing at least the purchase history data comprises processing user activity data on a community-specific basis for a plurality of communities.

8. The method of claim 1, wherein automatically elevating selected items for display comprises elevating the selected items along parent-child paths of the browse tree.

9. The method of claim 1, wherein automatically elevating selected items for display comprises generating item scores that are reflective of the node popularity data, and using the item scores to select items to elevate.

10. The method of claim 9, wherein generating item scores comprises applying a recommendation algorithm to data collected for a particular user.

11. The method of claim 1, wherein automatically elevating selected items for display comprises selecting items to be elevated on a user-specific basis.

12. The method of claim 1, wherein automatically elevating selected items for display comprises recursively selecting items to be elevated on a node-by-node basis.

13. The method of claim 1, wherein elevating selected items for display comprises displaying the selected items at a browse tree level at which the selected items ordinarily are not displayed.

14. The method of claim 1, further comprising elevating selected leaf categories for display based at least in-part on popularity levels of such leaf categories.

15. The method of claim 1, wherein the items elevated within the browse tree change over time in response to changes in the node popularity levels.

16. A computer system which embodies the method of claim 1.

17. A computer program which, when executed by a computer, performs the method of claim 1.

18. A web page generated according to the method of claim 1.