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.
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. 11. The system of claim 9 , wherein the processor is further configured to: select 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.