DETERMINING USER PREFERENCE OF ITEMS BASED ON USER RATINGS AND USER FEATURES

• US 20130054593A1
• Filed: 10/25/2012
• Published: 02/28/2013
• Est. Priority Date: 03/31/2009
• Status: Active Grant

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Park, Seung-Taek, Chu, Wei, Beaupre, Todd, Agarwal, Deepak K., Roy, Scott, Ramakrishnan, Raghu
• Granted Patent: US 8,909,626 B2
• Time in Patent Office: Days
• US Class Current: 707/736
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536

Abstract

A set of item-item affinities for a plurality of items is determined based on collaborative-filtering techniques. A set of an item'"'"'s nearest neighbor items based on the set of item-item affinities is determined. A set of user feature-item affinities for the plurality of items and a set of user features is determined based on least squared regression. A set of a user feature'"'"'s nearest neighbor items is determined based in part on the set of user feature-item affinities. Compatible affinity weights for nearest neighbor items of each item and each user feature are determined. Based on user features of a user and items a user has consumed, a set of nearest neighbor items are identified as a set of candidate items, and affinity scores of candidate items are determined. Based on the affinity scores, a candidate item from the set of candidate items is recommended to the user.

Claims

1. A method comprising:

determining a set of item-item affinities between a first item and a first plurality of items;

determining a first set of nearest neighbor items from the first plurality of items based, at least in part, on the set of item-item affinities;

determining a set of user feature-item affinities between a second plurality of items and a set of user features;

determining a second set of nearest neighbor items based, at least in part, on the set of user feature-item affinities;

determining affinity weights for a set of candidate items, wherein the set of candidate items is determined based, at least in part, on the first set of nearest neighbor items and the second set of nearest neighbor items;

causing to be presented, to a user as a recommendation, a candidate item from the set of candidate items, wherein the candidate item is determined based, at least in part, on an affinity weight of the candidate item; and

,

wherein the method is performed by one or more computing devices.

2. The method of claim 1, wherein the set of item-item affinities is determined by a cosine similarity function.

3. The method of claim 1, wherein the set of user feature-item affinities is determined by a least squares regression function.

4. The method of claim 1, wherein the set of item-item affinities is determined at least based in part on user ratings of the first plurality of items.

5. The method of claim 4 further comprising:

penalizing an item-item affinity within the set of item-item affinities if the item-item affinity is based on fewer than a minimum number of user ratings.

6. The method of claim 1, wherein at least one user feature from the set of user features is selected from the group consisting of user age, user gender, and geographic location.

7. The method of claim 1, wherein at least one user feature from the set of user features comprises behavioral targeting information.

8. The method of claim 1, further comprising:

recalculating a weighting of the first set of nearest neighbors items or the second set of nearest neighbor items.

9. The method of claim 1, wherein the affinity weight of the candidate item is based at least in part on a number of total users, a number of users who have rated the candidate item (N_(j)), a number of users who have an attribute (N_(s)), and a number of users who have the attribute and have also rated the candidate item (N_(s,j)), wherein N_(s) is one of a number of users who have a particular user feature or a number of users who have rated another item.

10. The method of claim 9, further comprising:

ignoring a second candidate item from the set of candidate items in response to N_(s,j) being below a value.

11. One or more machine-readable storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 1.

12. One or more machine-readable storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 2.

13. One or more machine-readable storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 3.

14. One or more machine-readable storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 4.

15. One or more machine-readable storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 5.

16. One or more machine-readable storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 6.

17. One or more machine-readable storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 7.

18. One or more machine-readable storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 8.

19. One or more machine-readable storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 9.

20. One or more machine-readable storage media storing instructions which, when executed by one or more processors, cause performance of the method recited in claim 10.