Methods, systems, and media for recommending content items based on topics

1. 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.

2. The method of claim 1 , wherein the plurality of content items includes a channel that provides content items.

3. The method of claim 1 , further comprising determining a conjunction that models interaction between the plurality of topics and the plurality of content items.

4. The method of claim 1 , wherein generating the model of user interests further comprises:

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

determining a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items.

5. The method of claim 1 , wherein generating the model of user interests further comprises:

determining related topics for each of the plurality of topics, wherein a distance value between a topic and a related topic is calculated;

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;

mapping the user interest information to at least one of the plurality of topic clusters to obtain user cluster features; and

determining a conjunction that models interaction between the user cluster features and the plurality of content items.

6. The method of claim 1 , wherein generating the model of user interests 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.

7. 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 the plurality of accessed content items is associated with a plurality of topics;

generates 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;

applies 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;

ranks the plurality of content items based on the determined probabilities; and

selects at least one of the plurality of content items to recommend to the user based on the ranked plurality of content items.

8. The system of claim 7 , wherein the plurality of content items includes a channel that provides content items.

9. The system of claim 7 , wherein the processor is further configured to determine a conjunction that models interaction between the plurality of topics and the plurality of content items.

10. The system of claim 7 , wherein the processor is further configured to:

select 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

determine a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items.

11. The system of claim 7 , wherein the processor is further configured to:

determine related topics for each of the plurality of topics, wherein a distance value between a topic and a related topic is calculated;

determine 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;

map the user interest information to at least one of the plurality of topic clusters to obtain user cluster features; and

determine a conjunction that models interaction between the user cluster features and the plurality of content items.

12. The system of claim 7 , wherein the processor is further configured to:

generate 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;

determine a subset of the plurality of topics based on the decision tree; and

determine a conjunction that models interaction between the subset of the plurality of topics and the 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.

14. The non-transitory computer-readable medium of claim 13 , wherein the plurality of content items includes a channel that provides content items.

15. The non-transitory computer-readable medium of claim 13 , wherein the method further comprises determining a conjunction that models interaction between the plurality of topics and the plurality of content items.

16. The non-transitory computer-readable medium of claim 13 , wherein the method further comprises:

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

determining a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items.

17. The non-transitory computer-readable medium of claim 13 , wherein the method further comprises:

determining related topics for each of the plurality of topics, wherein a distance value between a topic and a related topic is calculated;

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;

mapping the user interest information to at least one of the plurality of topic clusters to obtain user cluster features; and

determining a conjunction that models interaction between the user cluster features and the 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.