Methods, systems, and media for recommending content items based on topics
First Claim
1. A method for recommending content items, the method comprising:
- determining a plurality of accessed content items associated with a user, wherein each of a plurality of content items is associated with a plurality of topics;
determining the plurality of topics associated with each of the plurality of accessed content items;
generating a model of user interests based on the plurality of topics, wherein the model implements a machine learning technique to determine a plurality of weights for assigning to each of the plurality of topics and wherein the model of user interests is generated by;
retrieving a user interest profile that includes the plurality of topics associated with the plurality of content items accessed by the user and a plurality of other user interest profiles;
generating a decision tree, wherein a portion of the decision tree identifies which of the plurality of other user interest profiles are similar to the user interest profile;
determining a subset of the plurality of topics corresponding to the user interest profile and the similar user interest profiles in the portion of the decision tree;
determining a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items;
applying the model to determine, for the plurality of content items, a probability that the user watches a content item of the plurality of content items;
ranking the plurality of content items based on the determined probability; and
selecting a subset of the plurality of content items to recommend to the user based on the ranked plurality of content items.
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Abstract
Mechanisms for recommending content items based on topics are provided. In some implementations, a method for recommending content items is provided that includes: determining a plurality of accessed content items associated with a user, wherein each of the plurality of content items is associated with a plurality of topics; determining the plurality of topics associated with each of the plurality of accessed content items; generating a model of user interests based on the plurality of topics, wherein the model implements a machine learning technique to determine a plurality of weights for assigning to each of the plurality of topics; applying the model to determine, for a plurality of content items, a probability that the user would watch a content item of the plurality of content items; ranking the plurality of content items based on the determined probabilities; and selecting a subset of the plurality of content items to recommend to the user based on the ranked content items.
77 Citations
17 Claims
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1. A method for recommending content items, the method comprising:
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determining a plurality of accessed content items associated with a user, wherein each of a plurality of content items is associated with a plurality of topics; determining the plurality of topics associated with each of the plurality of accessed content items; generating a model of user interests based on the plurality of topics, wherein the model implements a machine learning technique to determine a plurality of weights for assigning to each of the plurality of topics and wherein the model of user interests is generated by; retrieving a user interest profile that includes the plurality of topics associated with the plurality of content items accessed by the user and a plurality of other user interest profiles; generating a decision tree, wherein a portion of the decision tree identifies which of the plurality of other user interest profiles are similar to the user interest profile; determining a subset of the plurality of topics corresponding to the user interest profile and the similar user interest profiles in the portion of the decision tree; determining a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items; applying the model to determine, for the plurality of content items, a probability that the user watches a content item of the plurality of content items; ranking the plurality of content items based on the determined probability; and selecting a subset of the plurality of content items to recommend to the user based on the ranked plurality of content items. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system for recommending content items, the system comprising:
a hardware processor that; determines a plurality of accessed content items associated with a user, wherein each of a plurality of content items is associated with a plurality of topics; determines the plurality of topics associated with each of the plurality of accessed content items; generates a model of user interests based on the plurality of topics, wherein the model implements a machine learning technique to determine a plurality of weights for assigning to each of the plurality of topics and wherein the model of user interests is generated by; retrieving a user interest profile that includes the plurality of topics associated with the plurality of content items accessed by the user and a plurality of other user interest profiles; generating a decision tree, wherein a portion of the decision tree identifies which of the plurality of other user interest profiles are similar to the user interest profile; determining a subset of the plurality of topics corresponding to the user interest profile and the similar user interest profiles in the portion of the decision tree; and determining a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items; applies the model to determine, for the plurality of content items, a probability that the user watches a content item of the plurality of content items; ranks the plurality of content items based on the determined probability; and selects a subset of the plurality of content items to recommend to the user based on the ranked plurality of content items. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the process to perform a method for recommending content items, the method comprising:
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determining a plurality of accessed content items associated with a user, wherein each of the plurality of content items is associated with a plurality of topics; determining the plurality of topics associated with each of the plurality of accessed content items; generating a model of user interests based on the plurality of topics, wherein the model implements a machine learning technique to determine a plurality of weights for assigning to each of the plurality of topics and wherein the model of user interests is generated by; retrieving a user interest profile that includes the plurality of topics associated with the plurality of content items accessed by the user and a plurality of other user interest profiles; generating a decision tree, wherein a portion of the decision tree identifies which of the plurality of other user interest profiles are similar to the user interest profile; determining a subset of the plurality of topics corresponding to the user interest profile and the similar user interest profiles in the portion of the decision tree; and determining a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items; applying the model to determine, for the plurality of content items, a probability that the user watches a content item of the plurality of content items; ranking the plurality of content items based on the determined probability; and selecting a subset of the plurality of content items to recommend to the user based on the ranked plurality of content items.
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Specification