Cluster-based assessment of user interests
First Claim
1. A computer-implemented method, comprising:
- identifying a collection of items associated with a target user;
applying a clustering algorithm to the collection of items to subdivide the collection into multiple clusters of items, wherein the clustering algorithm generates said clusters based, at least in part, on calculated distances between the items;
selecting a subset of the items in the collection to use as recommendation source items based, at least in part, on one or more attributes of the clusters of items;
using the selected recommendation source items as inputs to a recommendation engine to generate a set of recommended items for the target user; and
outputting a representation of at least some of the recommended items to the target user;
wherein the method, including identifying the collection of items, applying the clustering algorithm, selecting the subset of items, using the selected recommendation source items as inputs, and outputting said representation, is performed by a computer system.
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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.
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Citations
38 Claims
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1. A computer-implemented method, comprising:
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identifying a collection of items associated with a target user; applying a clustering algorithm to the collection of items to subdivide the collection into multiple clusters of items, wherein the clustering algorithm generates said clusters based, at least in part, on calculated distances between the items; selecting a subset of the items in the collection to use as recommendation source items based, at least in part, on one or more attributes of the clusters of items; using the selected recommendation source items as inputs to a recommendation engine to generate a set of recommended items for the target user; and outputting a representation of at least some of the recommended items to the target user; wherein the method, including identifying the collection of items, applying the clustering algorithm, selecting the subset of items, using the selected recommendation source items as inputs, and outputting said representation, is performed by a computer system. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A recommendation system, comprising:
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a computer data repository that stores a collection of items associated with a user; and a computer system programmed with executable components, including; a clustering component that applies a clustering algorithm to the collection of items to divide the collection into multiple clusters of items; a source selection component that selects items from said collection to use as recommendation sources, wherein the source selection component selects said items based, as least in part, on information regarding said clusters of items; and a recommendation engine that uses the source items selected by the source selection component to generate personalized item recommendations for the user. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26)
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27. A computer-implemented method, comprising:
by a computer system; identifying a collection of items associated with a user, said collection represented in computer storage; applying a clustering algorithm to the collection of items to subdivide the collection into a plurality of clusters of items, wherein the clustering algorithm generates said clusters based, at least in part, on calculated distances between the items; and selecting, from said collection of items, a subset of items to use as recommendation sources for generating personalized item recommendations for the user, wherein selecting the subset of items comprises analyzing the plurality of clusters. - View Dependent Claims (28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38)
Specification