Method, system, and medium for cluster-based categorization and presentation of item recommendations
First Claim
1. A computer-implemented method, comprising:
- identifying a collection of items associated with a user, said collection based on item selection actions of the user;
dividing the collection of items into clusters based, at least in part, upon calculated distances between items;
generating a set of recommended items for the user based, at least in part, on an analysis of the clusters using a computer;
dividing at least a portion of the set of recommended items into multiple clusters of recommended items, wherein the clusters of recommended items are generated based, at least in part, on calculated item distances;
assigning a category name to each cluster of recommended items; and
exposing each cluster of recommended items to the user in association with its respective assigned category name to facilitate selection of particular recommended items for consumption.
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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.
76 Citations
35 Claims
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1. A computer-implemented method, comprising:
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identifying a collection of items associated with a user, said collection based on item selection actions of the user; dividing the collection of items into clusters based, at least in part, upon calculated distances between items; generating a set of recommended items for the user based, at least in part, on an analysis of the clusters using a computer; dividing at least a portion of the set of recommended items into multiple clusters of recommended items, wherein the clusters of recommended items are generated based, at least in part, on calculated item distances; assigning a category name to each cluster of recommended items; and exposing each cluster of recommended items to the user in association with its respective assigned category name to facilitate selection of particular recommended items for consumption. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A computer system, comprising:
a computer data repository that stores an item collection associated with a user, said item collection based on item selection actions of the user; a first clustering component configured to divide the item collection into multiple clusters of collection items based, at least in part, on calculated item distances; a recommendation system configured to generate personalized item recommendations for the user based, at least in part, on the clusters of collection items; a second clustering component configured to use distance calculations to divide at least a portion of the personalized item recommendations into multiple clusters of recommended items, and to assign category names to said clusters of recommended items; and a server configured to expose the clusters of recommended items to the user in association with said category names to facilitate selection of particular recommended items for consumption. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34)
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35. Non-transitory computer storage that stores executable code that directs a computer system to perform a process that comprises:
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identifying a collection of items associated with a user, said collection based on item selection actions of the user; dividing the collection of items into clusters based, at least in part, upon calculated distances between items; generating a set of recommended items for the user based, at least in part, on an analysis of the clusters; dividing at least a portion of the set of recommended items into multiple clusters of recommended items, wherein the clusters of recommended items are generated based, at least in part, on calculated item distances; assigning a category name to each cluster of recommended items; and exposing each cluster of recommended items to the user in association with its respective assigned category name to facilitate selection of particular recommended items for consumption.
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Specification