SYSTEMS AND METHODS FOR SUPPLEMENTING CONTENT-BASED ATTRIBUTES WITH COLLABORATIVE RATING ATTRIBUTES FOR RECOMMENDING OR FILTERING ITEMS
First Claim
1. A method for recommending or filtering items, the method comprising:
- obtaining item interest data from users;
clustering the users into groups of users based on the interest data;
generating composite rating attribute values for each item, wherein each attribute value represents an aggregation of the interest data for the item for one or more of the groups of users;
creating one or more user preference models using the composite rating attribute values in conjunction with content-based attributes; and
at least one of recommending items to users and filtering items from users based on the user preference models.
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Abstract
Disclosed herein are systems and methods for supplementing content-based attributes with collaborative rating attributes for recommending or filtering items. Collaborative rating data may be consolidated into “composite critics” which serve as item quality rating attributes. These attributes may be used in conjunction with content-based attributes to generate user preference models. Composite critics may be formed using data clustering methods such that users with similar tastes may be grouped together. The user preference models may be induced using machine learning processes, such as decision trees, artificial neural networks, support vector machines, and/or statistical techniques. In some embodiments, composite critics may represent a small number of users or professional critics selected for having differing sensibilities and who rate most or all items according to those sensibilities.
48 Citations
36 Claims
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1. A method for recommending or filtering items, the method comprising:
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obtaining item interest data from users; clustering the users into groups of users based on the interest data; generating composite rating attribute values for each item, wherein each attribute value represents an aggregation of the interest data for the item for one or more of the groups of users; creating one or more user preference models using the composite rating attribute values in conjunction with content-based attributes; and at least one of recommending items to users and filtering items from users based on the user preference models. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A system for recommending or filtering items, the system comprising:
at least one processor for obtaining item interest data from users, clustering the users into groups of users based on the item interest data, generating composite rating attribute values for each item wherein each attribute value represents an aggregation of the interest data for the item for one or more of the groups of users, creating one or more user preference models using the composite rating attribute values in conjunction with content-based attributes, and at least one of recommending items to users and filtering items from users based on the user preference models. - View Dependent Claims (20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35)
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36. A method for recommending or filtering items, the method comprising:
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receiving collaborative rating data about items; generating composite critic attributes based the collaborative rating data; receiving item content data; extracting content attributes based on the item content data; compositing the generated composite critic attributes and the extracted content attributes into item vectors; generating user preference models based on the composited attributes in the item vectors; and using the user preference models to at least one of; recommend new items to one or more users, and classify, filter, or rate items in one or more learning or personalization applications.
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Specification