Patent Document ID: 9129227
Application ID: 13731266

Base 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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Claim 3:
3. The method of claim 1 , wherein generating the model of user interests further comprises: selecting the subset of the plurality of topics associated with each of the plurality of content items, wherein the subset of the plurality of topics is selected based on the plurality of assigned weights.