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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for recommending content items, the method comprising: retrieving, using a hardware processor, a first plurality of content items associated with a user account, wherein each of the first plurality of content items is associated with a plurality of topics; applying, using the hardware processor, to a second plurality of content items, a user interest model of interactions between the plurality of topics and the first plurality of content items to a second plurality of content items, wherein the application of the user interest model provides a probability that a user of the user account selects a content item from the second plurality of content items for presentation based on a plurality of related topics associated with the plurality of topics and user interest information associated with the user account using at least a portion of the plurality of related topics; and selecting, using the hardware processor, at least one of the plurality of content items to recommend to the user of the user account based on the determined probabilities. 2. The method of claim 1, wherein the user interest model is generated by (i) determining the plurality of related topics associated with the plurality of topics from the plurality of accessed content items, (ii) generating the user interest information associated with the user account using at least a portion of the plurality of related topics, (iii) determining similarities between the user interest information associated with the user account and user interest information of other users accounts including the at least a portion of the plurality of related topics associated with the user account, and (iv) determining a conjunction of between the similarities and the plurality of accessed content items. 3. The method of claim 1, wherein the user interest model is generated by (i) selecting a 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 a weight assigned to each of the plurality of topics, and (ii) determining a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items. 4. The method of claim 1, wherein user interest model is generated by (i) determining related topics for each of the plurality of topics, wherein a distance value between a topic and a related topic is calculated, (ii) determining a plurality of topic clusters, wherein one or more of the plurality of topics and one or more of the related topics are placed in a topic cluster based on the distance value, (iii) mapping the user interest information to at least one of the plurality of topic clusters to obtain user cluster features, and (iv) determining a conjunction that models interaction between the user cluster features and the plurality of content items. 5. The method of claim 1, wherein the user interest model is generated by (i) generating a decision tree, wherein a portion of the decision tree identifies which of the user interest information of other user accounts is similar to the user interest information of the user account, (ii) determining a subset of the plurality of topics based on the decision tree, and (iii) determining a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items. 6. The method of claim 1, further comprising ranking the second plurality of content items based on the determined probabilities, wherein the at least one of the second plurality of content items is selected based on the ranked plurality of content items. 7. A system for recommending content items, the system comprising: a hardware processor that: retrieves a first plurality of content items associated with a user account, wherein each of the first plurality of content items is associated with a plurality of topics; applies, to a second plurality of content items, a user interest model of interactions between the plurality of topics and the first plurality of content items to a second plurality of content items, wherein the application of the user interest model provides a probability that a user of the user account selects a content item from the second plurality of content items for presentation based on a plurality of related topics associated with the plurality of topics and user interest information associated with the user account using at least a portion of the plurality of related topics; and selects at least one of the plurality of content items to recommend to the user of the user account based on the determined probabilities. 8. The system of claim 7, wherein the user interest model is generated by (i) determining the plurality of related topics associated with the plurality of topics from the plurality of accessed content items, (ii) generating the user interest information associated with the user account using at least a portion of the plurality of related topics, (iii) determining similarities between the user interest information associated with the user account and user interest information of other users accounts including the at least a portion of the plurality of related topics associated with the user account, and (iv) determining a conjunction of between the similarities and the plurality of accessed content items. 9. The system of claim 7, wherein the user interest model is generated by (i) selecting a 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 a weight assigned to each of the plurality of topics, and (ii) determining a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items. 10. The system of claim 7, wherein user interest model is generated by (i) determining related topics for each of the plurality of topics, wherein a distance value between a topic and a related topic is calculated, (ii) determining a plurality of topic clusters, wherein one or more of the plurality of topics and one or more of the related topics are placed in a topic cluster based on the distance value, (iii) mapping the user interest information to at least one of the plurality of topic clusters to obtain user cluster features, and (iv) determining a conjunction that models interaction between the user cluster features and the plurality of content items. 11. The system of claim 7, wherein the user interest model is generated by (i) generating a decision tree, wherein a portion of the decision tree identifies which of the user interest information of other user accounts is similar to the user interest information of the user account, (ii) determining a subset of the plurality of topics based on the decision tree, and (iii) determining a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items. 12. The system of claim 7, wherein the hardware processor is further configured to rank the second plurality of content items based on the determined probabilities, wherein the at least one of the second plurality of content items is selected 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: retrieving a first plurality of content items associated with a user account, wherein each of the first plurality of content items is associated with a plurality of topics; applying, to a second plurality of content items, a user interest model of interactions between the plurality of topics and the first plurality of content items to a second plurality of content items, wherein the application of the user interest model provides a probability that a user of the user account selects a content item from the second plurality of content items for presentation based on a plurality of related topics associated with the plurality of topics and user interest information associated with the user account using at least a portion of the plurality of related topics; and selecting at least one of the plurality of content items to recommend to the user of the user account based on the determined probabilities. 14. The non-transitory computer-readable medium of claim 13, wherein the user interest model is generated by (i) determining the plurality of related topics associated with the plurality of topics from the plurality of accessed content items, (ii) generating the user interest information associated with the user account using at least a portion of the plurality of related topics, (iii) determining similarities between the user interest information associated with the user account and user interest information of other users accounts including the at least a portion of the plurality of related topics associated with the user account, and (iv) determining a conjunction of between the similarities and the plurality of accessed content items. 15. The non-transitory computer-readable medium of claim 13, wherein the user interest model is generated by (i) selecting a 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 a weight assigned to each of the plurality of topics, and (ii) determining a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items. 16. The non-transitory computer-readable medium of claim 13, wherein user interest model is generated by (i) determining related topics for each of the plurality of topics, wherein a distance value between a topic and a related topic is calculated, (ii) determining a plurality of topic clusters, wherein one or more of the plurality of topics and one or more of the related topics are placed in a topic cluster based on the distance value, (iii) mapping the user interest information to at least one of the plurality of topic clusters to obtain user cluster features, and (iv) determining a conjunction that models interaction between the user cluster features and the plurality of content items. 17. The non-transitory computer-readable medium of claim 13, wherein the user interest model is generated by (i) generating a decision tree, wherein a portion of the decision tree identifies which of the user interest information of other user accounts is similar to the user interest information of the user account, (ii) determining a subset of the plurality of topics based on the decision tree, and (iii) determining a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items. 18. The non-transitory computer-readable medium of claim 13, wherein the method further comprises ranking the second plurality of content items based on the determined probabilities, wherein the at least one of the second plurality of content items is selected based on the ranked plurality of content items.