RECOMMENDATION SYSTEM WITH CLUSTER-BASED FILTERING OF RECOMMENDATIONS
First Claim
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.
1 Assignment
0 Petitions
Accused Products
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.
-
Citations
21 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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
-
-
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. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21)
-
Specification