RECOMMENDER SYSTEM

• US 20130211950A1
• Filed: 02/09/2012
• Published: 08/15/2013
• Est. Priority Date: 02/09/2012
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Nice, Nir, Koenigstein, Noam, Paquet, Ulrich
• Granted Patent: US 10,438,268 B2
• Time in Patent Office: Days
• US Class Current: 705/26.7
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

Embodiments of the invention provide methods and apparatus for recommending items from a catalog of items to a user by parsing the catalog of items into a plurality of catalog clusters of related items and recommending catalog items to the user from catalog clusters to which items previously preferred by the user belong.

Claims

1. A method of recommending items to a user, the method comprising:

clustering catalog items in a catalog into a plurality of catalog clusters of catalog items;

acquiring data identifying legacy items previously preferred by the user;

identifying at least one catalog cluster of the plurality of catalog clusters to which a legacy item identified by the data belongs; and

recommending a catalog item from the at least one identified catalog clusters to the user.

2. A method according to claim 1 and using a metric that provides a measure of distance between any two catalog clusters of the plurality of clusters to recommend a catalog item to the user.

3. A method according to claim 2 wherein using the metric comprises determining distances between catalog clusters of the plurality of catalog clusters and identifying a catalog cluster of the plurality of catalog clusters to which a legacy item does not belong that has a determined distance less than a desired distance to at least one catalog cluster of the plurality of catalog clusters to which a legacy item does belong.

4. A method according to claim 3 and comprising recommending an item from the identified catalog cluster that does not comprise a legacy item.

5. A method according to claim 1 wherein clustering catalog items comprises acquiring rankings of the catalog items for a population of people and using the rankings to cluster the catalog items.

6. A method according to claim 1 wherein using the rankings comprises factoring a ranking matrix comprising the rankings to determine a trait vector to represent each catalog item.

7. A method according to claim 6 and comprising using the trait vectors to cluster the catalog items into the plurality of clusters.

8. A method according to claim 7 wherein using the trait vectors to cluster catalog items comprises clustering the trait vectors responsive to inner products of the trait vectors.

9. A method according to claim 8 wherein clustering responsive to the inner products comprises using a k-means algorithm.

10. A method according to claim 1 wherein clustering catalog items comprises using a collaborative filtering algorithm to cluster the catalog items.

11. A method according to claim 1 wherein clustering catalog items comprises using a content based algorithm to cluster the catalog items.

12. A method according to claim 1 wherein recommending comprises representing each catalog and each legacy item by a trait vector and recommending a catalog item responsive to a relationship between the trait vectors representing catalog and legacy items belonging to a same cluster.

13. A method according to claim 12 wherein using a relationship comprises:

determining a representative legacy trait vector to represent legacy items in a same catalog cluster;

determining inner products between the representative trait vector and trait vectors representing catalog items in the cluster; and

recommending a catalog item responsive to the inner products.

14. A recommender system comprising:

a database having data responsive to preferences exhibited by a population of users for catalog items in a catalog;

a cluster engine that generates a clustered database comprising a plurality of catalog clusters of catalog items responsive to data in the database; and

a recommender engine that identifies at least one catalog cluster of the plurality of catalog clusters to which a legacy item previously preferred by a user belongs and recommends a catalog item from the at least one identified catalog cluster to the user.

15. A recommender system according to claim 14 and comprising a model maker that provides a representation for each catalog item that the cluster engine, which the uses to generate the clustered database.

16. A recommender system according to claim 15 wherein the model maker models the catalog items as trait vectors in a latent space and generates a representation for each catalog item that comprises a trait vector.

17. A recommender system according to claim 16 wherein the cluster engine uses the trait vectors to generate the clustered database.

18. A recommender system according to claim 14 wherein the cluster engine clusters catalog items using a collaborative filtering algorithm.

19. A recommender system according to claim 14 wherein the cluster engine clusters catalog items using a content based algorithm.

20. A recommender system according to claim 14 wherein the recommender engine:

determines distances between catalog clusters of the plurality of clusters responsive to a metric;

identifies a catalog cluster of the plurality of catalog clusters to which a legacy item does not belong and which has a determined distance less than a desired distance to at least one catalog cluster of the plurality of catalog clusters to which a legacy item does belong; and

recommends a catalog item from the identified catalog cluster to which a legacy item does not belong.