RECOMMENDATION SYSTEM WITH CLUSTER-BASED FILTERING OF RECOMMENDATIONS

• US 20080243637A1
• Filed: 03/30/2007
• Published: 10/02/2008
• Est. Priority Date: 03/30/2007
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: Ionkov, George M., Chan, James D., Chakrabarti, Kushal
• Granted Patent: US 8,095,521 B2
• Time in Patent Office: Days
• US Class Current: 705/27
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

Computer-implemented processes are disclosed for clustering items and improving the utility of item recommendations. One process involves applying a clustering algorithm to a user'"'"'s collection of items. Information about the resulting clusters is then used to select items to use as recommendation sources. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to attach cluster-level metadata, such as by rating or tagging entire clusters of items. The resulting metadata may be used to improve the recommendations generated by a recommendation engine. Another process involves forming clusters of items in which a user has indicated a lack of interest, and using these clusters to filter the output of a recommendation engine. Yet another process involves applying a clustering algorithm to the output of a recommendation engine to arrange the recommended items into cluster-based categories for presentation to the user.

Claims

1. A computer-implemented method, comprising:

maintaining a collection of items in association with a user, said collection comprising items rated by the user;

applying a clustering algorithm to said collection of items to subdivide the collection into multiple clusters, wherein the clusters are generated based, at least in part, on calculated distances between the items;

obtaining a set of recommended items for the user from a recommendation engine;

filtering the set of recommended items based, at least in part, on distances between the recommended items and one or more of said clusters of items, wherein filtering the set of recommended items comprises filtering out at least one item from the set of recommended items to generate a filtered set of recommended items; and

outputting the filtered set of recommended items for presentation to the user.

2. The method of claim 1, wherein the items are represented in a hierarchical browse structure, and wherein applying the clustering algorithm comprises using locations of the items in the hierarchical browse structure to calculate said distances.

3. The method of claim 2, wherein the clustering component calculates an entropy level of a cluster, and uses said entropy level to determine whether to divide the cluster.

4. The method of claim 1, wherein filtering the set of recommended items comprises filtering out a recommended item based, at least in part, on its distance from a cluster representing an area in which the user lacks interest.

5. The method of claim 1, wherein the method comprises using ratings assigned to items in a cluster to assess whether the cluster represents an area in which the user lacks interest.

6. The method of claim 5, further comprising, when the cluster is determined to represent an area in which the user lacks interest, using said cluster to filter out recommended items associated with the cluster.

7. The method of claim 1, wherein the distances between the recommended items and one or more of said clusters are calculated based on centers of said clusters.

8. The method of claim 1, wherein the collection includes or consists of items in which the user has explicitly indicated a lack of interest.

9. The method of claim 1, further comprising using at least two of the clusters to organize the filtered set of recommended items into cluster-based categories for presentation to the user.

10. A computer system programmed to perform the method of claim 1.

11. A computer-readable medium having stored thereon executable computer code that embodies the method of claim 1.

12. A computer system, comprising:

a computer data repository that stores a collection of items associated with a user;

a clustering component that applies a clustering algorithm to the collection of items to divide the collection into multiple clusters of items;

a recommendation engine that generates a set of personalized item recommendations for the user; and

a filtering component that filters out selected items from the set of personalized item recommendations based, at least in part, on distances between said items and particular item clusters generated by the clustering component.

13. The computer system of claim 12, wherein the clustering component uses browse node ancestries of the items as at least one criterion for forming said clusters of items.

14. The computer system of claim 12, wherein the collection comprises items rated by the user, and the clustering component uses item ratings assigned by the user to identify a cluster that represents an area in which the user lacks interest.

15. The computer system of claim 14, wherein filtering component filters out items that are deemed to be associated with said cluster that represents an area in which the user lacks interest.

16. The computer system of claim 12, further comprising a presentation component that outputs the filtered set of item recommendations for presentation to the user.

17. The computer system of claim 16, wherein the presentation component uses clusters generated by the clustering component to organize the filtered set of personalized item recommendation into multiple categories for presentation to the user.

18. The computer system of claim 12, wherein the collection includes or consists of items in which the user has explicitly indicated a lack of interest.

19. The computer system of claim 12, further comprising a user interface that enables the user to assign a tag to multiple items at-a-time by tagging a selected one of said clusters, and enables the user to request items recommendations that are specific to said tag.

20. The computer system of claim 12, further comprising a user interface that enables the user to request items recommendations that are specific to a selected one of said clusters.

21. The computer system of claim 12, wherein the recommendation engine generates the set of personalized item recommendations based, at least in part, on items included in said collection.