13. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for recommending content items, the method comprising: determining a plurality of accessed content items associated with a user, wherein each of the plurality of accessed content items is associated with a plurality of topics; generating a user interest model of interactions between the plurality of topics and the plurality of accessed content items, wherein the user interest model (i) determines a plurality of related topics associated with the plurality of topics from the plurality of accessed content items, (ii) generates user interest information associated with the user using at least a portion of the plurality of related topics, (iii) determines similarities between the user interest information associated with the user and user interest information of other users including the at least a portion of the plurality of related topics associated with the user, and (iv) determines a conjunction of between the similarities and the plurality of accessed content items; applying the model to determine, for a plurality of content items, a probability that the user selects a content item from the plurality of content items for presentation; ranking the plurality of content items based on the determined probabilities; and selecting at least one of the plurality of content items to recommend to the user based on the ranked plurality of content items.
13. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for recommending content items, the method comprising: determining a plurality of accessed content items associated with a user, wherein each of the plurality of accessed content items is associated with a plurality of topics; generating a user interest model of interactions between the plurality of topics and the plurality of accessed content items, wherein the user interest model (i) determines a plurality of related topics associated with the plurality of topics from the plurality of accessed content items, (ii) generates user interest information associated with the user using at least a portion of the plurality of related topics, (iii) determines similarities between the user interest information associated with the user and user interest information of other users including the at least a portion of the plurality of related topics associated with the user, and (iv) determines a conjunction of between the similarities and the plurality of accessed content items; applying the model to determine, for a plurality of content items, a probability that the user selects a content item from the plurality of content items for presentation; ranking the plurality of content items based on the determined probabilities; and selecting at least one of the plurality of content items to recommend to the user based on the ranked plurality of content items. 18. The non-transitory computer-readable medium of claim 13 , wherein the method further comprises: generating a decision tree, wherein a portion of the decision tree identifies which of the user interest information of other users is similar to the user interest information of the user; determining a subset of the plurality of topics based on the decision tree; and determining a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items.