CLUSTER-BASED MANAGEMENT OF COLLECTIONS OF ITEMS

• US 20080243817A1
• 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 7,743,059 B2
• Time in Patent Office: Days
• US Class Current: 707/5
• 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
• 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 of facilitating user management of a collection of items, the method comprising:

maintaining an item collection of a user in computer storage;

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

displaying the multiple clusters to the user via an interface that enables the user to associate metadata with specific clusters of items, and receiving cluster-level metadata from the user, said cluster-level metadata corresponding to a cluster selected by the user, said cluster comprising a plurality of items;

using the cluster-level metadata, in combination with data regarding the collection of items, to generate personalized item recommendations for the user; and

outputting a representation of the personalized item recommendations for presentation to the user.

2. The method of claim 1, wherein the item collection includes or consists of items purchased by the user.

3. The method of claim 1, wherein the item collection includes or consists of items rated by the user.

4. 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.

5. The method of claim 1, wherein the cluster-level metadata is a rating assigned by the user to said cluster.

6. The method of claim 5, wherein the step of using the cluster-level metadata comprises using the cluster-level rating to do at least one of the following:

(1) determine whether to use items in said cluster as recommendation sources for generating said item recommendations, (2) determine how much weight to give to items in said cluster in generating said item recommendations.

7. The method of claim 1, wherein the cluster-level metadata is a tag assigned by the user to the cluster to collectively tag the items in said cluster.

8. The method of claim 7, wherein the method comprises selecting items tagged with said tag to use as recommendation sources for generating tag-specific item recommendations.

9. The method of claim 1, wherein the cluster-level metadata represents an indication by the user of a least one of the following:

(a) the user lacks interest in the cluster, (b) the cluster should not be used to generate recommendations.

10. The method of claim 1, wherein the method comprises using at least the cluster-level metadata to select items from the collection to use as recommendation sources for generating said item recommendations.

11. The method of claim 1, further comprising presenting the user an option to request and obtain item recommendation that are specific to a particular one of said clusters.

12. The method of claim 1, further comprising presenting the user an option to assign a category name to a cluster, and using said category name to arrange the item recommendations for presentation to the user.

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

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

15. 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 collection management interface that includes functionality for the user to view the clusters of items, and to associate metadata with a selected cluster of items, said cluster including multiple items; and

a recommendation system that uses said metadata, in combination with data regarding the collection of items, to generate personalized item recommendations for the user.

16. The computer system of claim 15, wherein the item collection includes or consists of items purchased by the user.

17. The computer system of claim 15, wherein the item collection includes or consists of items purchased by the user.

18. The computer system of claim 15, wherein the items are represented in a hierarchical browse structure, and wherein the clustering component uses browse node ancestries of the items to calculate distances between the items, and uses said distances to form said clusters.

19. The computer system of claim 15, wherein the collection management interface provides functionality for the user to assign ratings to particular clusters, and the recommendation system takes said ratings into consideration in generating the personalized item recommendations.

20. The computer system of claim 19, wherein recommendation engine uses a rating assigned by the user to a cluster that comprises multiple items to do at least one of the following:

(1) determine whether to use items in said cluster as recommendation sources for generating said item recommendations, (2) determine how much weight to give to items in said cluster in generating said item recommendations.

21. The computer system of claim 15, wherein the collection management interface provides functionality for the user to assign tags to particular clusters of items, and the recommendation system takes said tags into consideration in generating the personalized item recommendations.

22. The computer system of claim 15, wherein the collection management interface provides functionality for the user to request and obtain item recommendation that are specific to a particular one of said clusters

23. The computer system of claim 15, wherein the collection management interface provides functionality for the user to assign a category name to a cluster, and the recommendation system uses said category name to present associated item recommendation to the user.

24. The computer system of claim 15, further comprising a recommendation categorization component that uses the clusters generated by the clustering component to subdivide the personalized item recommendations into multiple cluster-based categories for presentation to the user.