Systems, methods, and non-transitory computer-readable media can determine a respective latent representation for each entity in a set of entities that are accessible through the social networking system, wherein a latent representation for an entity is determined based at least in part on a topic model associated with the entity, each latent representation for an entity having a lower dimensionality than a topic model of the entity. One or more candidate entities that are related to a first entity can be determined based at least in part on the respective latent representations for the candidate entities and the first entity. At least a first candidate entity from the one or more candidate entities can be provided as a recommendation to a user that formed a connection with the first entity.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: determining, by a social networking system, a respective latent representation for each entity in a set of entities that are accessible through the social networking system, wherein a latent representation for an entity is determined based at least in part on a topic model associated with the entity, each latent representation for an entity having a lower dimensionality than a topic model of the entity; determining, by the social networking system, one or more candidate entities that are related to a first entity based at least in part on the respective latent representations for the candidate entities and the first entity; and providing, by the social networking system, at least a first candidate entity from the one or more candidate entities as a recommendation to a user that formed a connection with the first entity. 2. The computer-implemented method of claim 1, wherein determining the respective latent representation for each entity in the set of entities further comprises: obtaining, by the social networking system, a respective topic model for each entity in the set of entities; and training, by the social networking system, at least one model using the topic models to output a respective latent representation that corresponds to a topic model. 3. The computer-implemented method of claim 2, wherein the at least one model is implemented as a restricted Boltzmann machine. 4. The computer-implemented method of claim 2, wherein the model includes at least a set of input nodes and a set of hidden nodes, each input node corresponding to a topic and being configured to receive a value indicating whether the topic was identified in an entity, and each hidden node being configured to output a value determined based at least in part on values provided to one or more of the input nodes. 5. The computer-implemented method of claim 1, wherein determining the one or more candidate entities that are related to the first entity further comprises: determining, by the social networking system, that a distance between a respective latent representation for the candidate entity and a latent representation for the first entity satisfies a threshold distance. 6. The computer-implemented method of claim 1, wherein providing at least the first candidate entity from the one or more candidate entities as a recommendation further comprises: determining, by the social networking system, that at least one geographic centroid associated with the first candidate entity has a threshold amount of overlap with at least one geographic centroid associated with the first entity. 7. The computer-implemented method of claim 1, wherein providing at least the first candidate entity from the one or more candidate entities as a recommendation further comprises: determining, by the social networking system, that a reconstruction score for the first candidate entity satisfies a threshold score, the reconstruction score being determined using a model trained to output a latent representation for the first candidate entity, wherein the reconstruction score measures an accuracy of a reconstruction of a topic model of the first candidate entity through the model using the latent representation. 8. The computer-implemented method of claim 1, wherein providing at least the first candidate entity from the one or more candidate entities as a recommendation further comprises: determining, by the social networking system, that a difference between a number of fans associated with the first candidate entity and a number of fans associated with the first entity satisfies a threshold value. 9. The computer-implemented method of claim 1, wherein providing at least the first candidate entity from the one or more candidate entities as a recommendation further comprises: determining, by the social networking system, that at least a threshold number of users have fanned both the first candidate and the first entity. 10. The computer-implemented method of claim 1, wherein an entity corresponds to at least a page, user profile, group, story, or status update that is accessible through the social networking system. 11. A system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: determining a respective latent representation for each entity in a set of entities that are accessible through the social networking system, wherein a latent representation for an entity is determined based at least in part on a topic model associated with the entity, each latent representation for an entity having a lower dimensionality than a topic model of the entity; determining one or more candidate entities that are related to a first entity based at least in part on the respective latent representations for the candidate entities and the first entity; and providing at least a first candidate entity from the one or more candidate entities as a recommendation to a user that formed a connection with the first entity. 12. The system of claim 11, wherein determining the respective latent representation for each entity in the set of entities further causes the system to perform: obtaining a respective topic model for each entity in the set of entities; and training at least one model using the topic models to output a respective latent representation that corresponds to a topic model. 13. The system of claim 12, wherein the at least one model is implemented as a restricted Boltzmann machine. 14. The system of claim 12, wherein the model includes at least a set of input nodes and a set of hidden nodes, each input node corresponding to a topic and being configured to receive a value indicating whether the topic was identified in an entity, and each hidden node being configured to output a value determined based at least in part on values provided to one or more of the input nodes. 15. The system of claim 11, wherein determining the one or more candidate entities that are related to the first entity further causes the system to perform: determining that a distance between a respective latent representation for the candidate entity and a latent representation for the first entity satisfies a threshold distance. 16. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising: determining a respective latent representation for each entity in a set of entities that are accessible through the social networking system, wherein a latent representation for an entity is determined based at least in part on a topic model associated with the entity, each latent representation for an entity having a lower dimensionality than a topic model of the entity; determining one or more candidate entities that are related to a first entity based at least in part on the respective latent representations for the candidate entities and the first entity; and providing at least a first candidate entity from the one or more candidate entities as a recommendation to a user that formed a connection with the first entity. 17. The non-transitory computer-readable storage medium of claim 16, wherein determining the respective latent representation for each entity in the set of entities further causes the computing system to perform: obtaining a respective topic model for each entity in the set of entities; and training at least one model using the topic models to output a respective latent representation that corresponds to a topic model. 18. The non-transitory computer-readable storage medium of claim 17, wherein the at least one model is implemented as a restricted Boltzmann machine. 19. The non-transitory computer-readable storage medium of claim 17, wherein the model includes at least a set of input nodes and a set of hidden nodes, each input node corresponding to a topic and being configured to receive a value indicating whether the topic was identified in an entity, and each hidden node being configured to output a value determined based at least in part on values provided to one or more of the input nodes. 20. The non-transitory computer-readable storage medium of claim 16, wherein determining the one or more candidate entities that are related to the first entity further causes the system to perform: determining that a distance between a respective latent representation for the candidate entity and a latent representation for the first entity satisfies a threshold distance.