PROCESSES FOR ASSESSING USER AFFINITIES FOR PARTICULAR ITEM CATEGORIES OF A HIERARCHICAL BROWSE STRUCTURE

• US 20110022606A1
• Filed: 10/07/2010
• Published: 01/27/2011
• Est. Priority Date: 10/13/2003
• Status: Active Grant

• Current Assignee: Zachary J. Mason
• Original Assignee: Zachary J. Mason
• Inventors: Mason, Zachary J.
• Granted Patent: US 7,979,445 B2
• Time in Patent Office: Days
• US Class Current: 707/748
• 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

Abstract

A computer-implemented system provides a browse tree in which items represented within a database are arranged within a hierarchy of item categories. Each time a user selects an item, an amount of credit is cumulatively assigned to the ancestor nodes (categories) of the selected item. The amount of credit assigned to a particular category of the browse tree over time for a given user represents the user'"'"'s predicted affinity for that category. The user'"'"'s relative preferences for some or all of the categories are predicted by calculating differences between the user'"'"'s predicted affinities for such categories and the predicted affinities of a population of users for such categories. Scores reflective of these relative category preferences are used to provide personalized recommendations or other personalized content to the user.

Claims

1. A computer-implemented method, comprising:

recording item selection events in which users select particular items represented in an electronic data repository, said items arranged in a hierarchical browse structure that comprises multiple levels of item categories;

generating relative preference profiles for particular users, each relative preference profile representing preferences of a respective user for particular item categories of said hierarchical browse structure relative to corresponding preference levels of a population of users, wherein generating the relative preference profiles comprises analyzing the recorded item selection events to assess users'"'"' preferences for particular item categories; and

selecting items to recommend to users based at least partly on the relative preference profiles of the users;

said method performed in its entirely by a computer system.

2. The method of claim 1, wherein selecting items to recommend to users comprises identifying similar users based on comparisons of the relative preference profiles of the users, and using the identified similar users to implement a collaborative recommendations algorithm.

3. The method of claim 1, wherein generating the relative preference profiles comprises generating relative preference scores representing a user'"'"'s relative preferences for respective item categories relative to preferences of said user population.

4. The method of claim 3, wherein generating the relative preference scores comprises:

generating a first normalized score representing a preference level of the user for an item category, said normalized score based on item selections of the user;

generating a second normalized score representing a preference level of the user population for said item category, said second normalized score based on item selections of a plurality of users; and

comparing the first normalized score to the second normalized score to determine a relative preference score for the item category.

5. The method of claim 4, wherein comparing the first normalized score to the second normalized score comprises applying an entropy function to the first and second normalized scores.

6. The method of claim 1, wherein generating the relative preference profiles comprises:

determining an amount of credit to be distributed for an item selection event in which a user selected an item; and

distributing said amount of credit among a plurality of item categories under which the item falls in said hierarchical browse structure.

7. The method of claim 1, wherein the computer system comprises a plurality of servers.

8. An interactive system, comprising:

a server system coupled to a communications network, said server system configured to provide interactive user access to a hierarchical browse structure in which items represented in a data repository are arranged in a hierarchy of item categories, said hierarchy comprising multiple levels of item categories, said server system configured to record item selection events in which users select particular items represented in the data repository;

an analysis component configured to use the recorded item selection events to generate relative preference profiles for particular users, each relative preference profile representing preferences of a respective user for particular item categories of said hierarchy relative to corresponding category preference levels of a population of users; and

a recommendation module configured to use the relative preference profiles to select items to recommend to particular users.

9. The interactive system of claim 8, wherein the interactive system is configured to identify similar users based on comparisons of the relative preference profiles of the users.

10. The interactive system of claim 8, wherein the interactive system is configured to implement a collaborative recommendations process in which users are matched to similar users based on comparisons of the relative preference profiles of the users.

11. The interactive system of claim 8, wherein the recommendation module is configured to rank a set of item recommendations for presentation to a user based at least partly on the relative preference profile of the user.

12. The interactive system of claim 8, wherein the analysis component is configured to generate relative preference scores representing a user'"'"'s relative preferences for respective item categories relative to preferences of said user population.

13. The interactive system of claim 8, wherein the analysis component is configured to generate the relative preference scores by a process that comprises:

generating a first normalized score representing a preference level of the user for an item category, said normalized score based on item selections of the user;

generating a second normalized score representing a preference level of the user population for said item category; and

comparing the first normalized score to the second normalized score to determine a relative preference score for the item category.

14. The interactive system of claim 8, wherein the analysis component is configured to compare the first normalized score to the second normalized score using a relative entropy function.

15. The interactive system of claim 8, wherein the analysis component is configured to assess a user'"'"'s preferences for particular item categories by a process that comprises:

determining an amount of credit to be distributed for an item selection event in which the user selected an item; and

distributing said amount of credit among a plurality of item categories under which the item falls in said hierarchical browse structure.

16. The interactive system of claim 8, wherein the items are products represented in an electronic catalog.

17. A method of assessing a user'"'"'s preferences for particular item categories of a hierarchical browse structure, the method comprising:

determining a total amount of credit to be distributed for an item selection event in which the user selected an item that is categorized in the hierarchical browse structure;

identifying a plurality of ancestor categories of the selected item in the hierarchical browse structure;

dividing said total amount of credit by the number of ancestor categories of the selected item to determine an amount of credit per ancestor category to be distributed for the item selection event; and

assigning said amount of credit per ancestor category to the ancestor categories;

said method performed in its entirely by a computerized system.

18. The method of claim 17, wherein determining the total amount of credit comprises selecting an amount that depends upon a type of the item selection event, such that different total credit amounts are used for different types of item selection events.

19. The method of claim 17, wherein the item selection event comprises viewing an item, and wherein determining the total amount of credit comprises selecting a total credit amount that depends on an amount of time spent by the user viewing the item.

20. Physical computer storage which stores executable code that directs a computer-based system to perform a method that comprising:

determining a total amount of credit to be distributed for an item selection event in which a user selects an item that is categorized in a hierarchical browse structure that comprises multiple levels of item categories, said total amount of credit being based at least partly on a type of the item selection event;

identifying a plurality of ancestor categories of the selected item in the hierarchical browse structure; and

dividing said total amount of credit among the plurality of ancestor categories.