SYSTEMS AND METHODS FOR SUPPLEMENTING CONTENT-BASED ATTRIBUTES WITH COLLABORATIVE RATING ATTRIBUTES FOR RECOMMENDING OR FILTERING ITEMS

• US 20120323725A1
• Filed: 12/14/2011
• Published: 12/20/2012
• Est. Priority Date: 12/15/2010
• Status: Abandoned Application

• Current Assignee: Fourthwall Media Incorporated
• Original Assignee: Fourthwall Media Incorporated
• Inventors: JOHNSTON, Jeffrey W., Slothouber, Louis P.
• Application Number:
  US13/325,717
• Publication Number:
  US 20120323725A1
• Time in Patent Office: Days
• US Class Current: 705/26.7
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Disclosed herein are systems and methods for supplementing content-based attributes with collaborative rating attributes for recommending or filtering items. Collaborative rating data may be consolidated into “composite critics” which serve as item quality rating attributes. These attributes may be used in conjunction with content-based attributes to generate user preference models. Composite critics may be formed using data clustering methods such that users with similar tastes may be grouped together. The user preference models may be induced using machine learning processes, such as decision trees, artificial neural networks, support vector machines, and/or statistical techniques. In some embodiments, composite critics may represent a small number of users or professional critics selected for having differing sensibilities and who rate most or all items according to those sensibilities.

Claims

1. A method for recommending or filtering items, the method comprising:

obtaining item interest data from users;

clustering the users into groups of users based on the interest data;

generating composite rating attribute values for each item, wherein each attribute value represents an aggregation of the interest data for the item for one or more of the groups of users;

creating one or more user preference models using the composite rating attribute values in conjunction with content-based attributes; and

at least one of recommending items to users and filtering items from users based on the user preference models.

2. The method of claim 1, wherein the items are at least one of television programs, movies, music, books, documents, products, services, e-mails, and advertisements.

3. The method of claim 1, wherein the item interest data are represented as binary preference values.

4. The method of claim 1, wherein the item interest data are represented as a multi-valued range of ratings.

5. The method of claim 1, wherein obtaining item interest data comprises obtaining data from a plurality of users whose item interest data are gathered over time via a collaborative-filtering system, wherein the item interest data are gathered implicitly, explicitly, or a combination thereof.

6. The method of claim 1, wherein obtaining item interest data comprises obtaining item interest data from users selected for having differing sensibilities, wherein these users serve as a de facto groups of users.

7. The method of claim 1, wherein clustering the users into groups comprises at least one of a hierarchical and partitional process based on an assessment of similarity of ratings between users.

8. The method of claim 7, wherein the similarity is calculated as:

r_xyd_s where r_xy is the correlation coefficient,

9. The method of claim 1, wherein clustering the users into groups is fuzzy such that a user may be included in multiple groups.

10. The method of claim 1, wherein generating a composite rating attribute value comprises averaging interest values of all the users in a given group and substituting a special value for those items having zero or a small number of ratings.

11. The method of claim 10, wherein the special value is the global average interest value of all items in the group.

12. The method of claim 10, wherein the special value is at least one of:

the global average interest value of all items in the user group for items having zero ratings, and

the global average interest value averaged in with the existing item ratings for items having one or more ratings.

13. The method of claim 10, wherein the special value is equal to the average of the lowest quartile of values for items over the entire group.

14. The method of claim 10, wherein the special value is generated by an induction process using content-based attributes alone wherein the average interest values for each item rated by users are used as target independent variables and content-based attributes are used as dependent variables during a training phase, and the resulting user preference model is used to valuate the special value.

15. The method of claim 1, wherein generating composite rating attribute values further comprises generating additional attributes that indicate at least one of:

the total number of raters used to generate each composite rating attribute value for each item in each group, and

the distribution of ratings used to generate each composite rating attribute value for each item in each group.

16. The method of claim 1, wherein creating user preference models utilizes at least one of a decision tree, artificial neural network, support vector machine, regression tree, decision tree with linear models in leaves, machine learning induction mechanism, and statistical process.

17. The method of claim 1, wherein the at least one of recommending and filtering comprises:

submitting vectors for new items having composite rating attribute values and content attribute values to the previously-generated user model, and

receiving a classification value or predicted rating as output.

18. A computer readable medium comprising code to perform the acts of the method of claim 1.

19. A system for recommending or filtering items, the system comprising:

at least one processor for obtaining item interest data from users, clustering the users into groups of users based on the item interest data, generating composite rating attribute values for each item wherein each attribute value represents an aggregation of the interest data for the item for one or more of the groups of users, creating one or more user preference models using the composite rating attribute values in conjunction with content-based attributes, and at least one of recommending items to users and filtering items from users based on the user preference models.

20. The system of claim 19, wherein the items are at least one of television programs, movies, music, books, documents, products, services, e-mails, and advertisements.

21. The system of claim 19, wherein the item interest data are represented as binary preference values.

22. The system of claim 19, wherein the item interest data are represented as a multi-valued range of ratings.

23. The system of claim 19, wherein obtaining item interest data comprises obtaining data from a plurality of users whose item interest data are gathered over time via a collaborative-filtering system, wherein the item interest data are gathered implicitly, explicitly, or a combination thereof.

24. The system of claim 19, wherein obtaining item interest data comprises obtaining item interest data from users selected for having differing sensibilities, wherein these users serve as de facto groups of users

25. The system of claim 19, wherein clustering the users into groups comprises at least one of a hierarchical and partitional process based on an assessment of similarity of ratings between users.

26. The system of claim 25, wherein the similarity is calculated as:

r_xyd_s where r_xy is the correlation coefficient,

27. The system of claim 19, wherein clustering the users into groups is fuzzy such that a user may be included in multiple groups.

28. The system of claim 19, wherein generating a composite rating attribute value comprises averaging interest values of all the users in each group and substituting a special value for those items having zero or a small number of ratings.

29. The system of claim 28, wherein the special value is the global average interest value of all items in the group.

30. The system of claim 28, wherein the special value is at least one of:

the global average interest value of all items in the user group for items having zero ratings, and

the global average interest value averaged in with the existing item ratings for items having one or more ratings.

31. The system of claim 28, wherein the special value is equal to the average of the lowest quartile of values for items over the entire group.

32. The system of claim 28, wherein the special value is generated by an induction process using content-based attributes alone wherein the average interest values for each item rated by users are used as target independent variables and content-based attributes are used as dependent variables during a training phase, and the resulting user preference model is used to valuate the special value.

33. The system of claim 19, wherein generating composite rating attribute values further comprises generating additional attributes that indicate at least one of:

the total number of raters used to generate each composite rating attribute value for each item in each group, and

the distribution of ratings used to generate each composite rating attribute value for each item in each group.

34. The system of claim 19, wherein creating user preference models utilizes at least one of a decision tree, artificial neural network, support vector machine, regression tree, decision tree with linear models in leaves, machine learning induction mechanism, and statistical process.

35. The system of claim 19, wherein the at least one of recommending and filtering comprises:

submitting vectors for new items having composite rating attribute values and content attribute values to the previously-generated user model, and

receiving a classification value or predicted rating as output.

36. A method for recommending or filtering items, the method comprising:

receiving collaborative rating data about items;

generating composite critic attributes based the collaborative rating data;

receiving item content data;

extracting content attributes based on the item content data;

compositing the generated composite critic attributes and the extracted content attributes into item vectors;

generating user preference models based on the composited attributes in the item vectors; and

using the user preference models to at least one of;

recommend new items to one or more users, and

classify, filter, or rate items in one or more learning or personalization applications.