Systems, methods, and non-transitory computer readable media are configured to determine a feature set for a model to be trained by machine learning. A subset of features from the feature set can be associated with entities having relationship types and corresponding to pages on a social networking system. The feature set can be reduced based on at least one rule applied to the relationship types.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: determining, by a computing system, a feature set for a model to be trained by machine learning; associating, by the computing system, a subset of features from the feature set with entities having relationship types and corresponding to pages on a social networking system; and reducing, by the computing system, the feature set based on at least one rule applied to the relationship types. 2. The computer-implemented method of claim 1, wherein application of the at least one rule to the relationship types is subject to a condition based on interactions with the pages. 3. The computer-implemented method of claim 2, further comprising: determining counts of the interactions with the pages. 4. The computer-implemented method of claim 3, wherein the relationship types include a relationship involving a child entity having a parent entity. 5. The computer-implemented method of claim 4, wherein the at least one rule provides that the child entity is selected as a feature for the child entity when the condition is satisfied and wherein the condition requires that the selected child entity have a count of interactions that is a predetermined factor higher than a count of interactions of the parent entity. 6. The computer-implemented method of claim 3, wherein the relationship types include a relationship involving a child entity having a set of parent entities. 7. The computer-implemented method of claim 6, wherein the at least one rule provides that a parent entity from the set of parent entities is selected as a feature for the child entity when the condition is satisfied and wherein the condition requires that the selected parent entity have a highest count of page interactions among all parent entities in the set of parent entities. 8. The computer-implemented method of claim 3, wherein the relationship types include a relationship involving a set of entities in a cyclic relationship. 9. The computer-implemented method of claim 8, wherein the at least one rule provides that an entity from the set of entities is selected as a feature for all of the entities in the set of entities when the condition is satisfied and wherein the condition requires that the selected entity have a highest count of page interactions among all entities in the set of entities. 10. The computer-implemented method of claim 1, wherein the relationship types are initially determined by at least one of automated analysis or manual curation of information sources. 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 feature set for a model to be trained by machine learning; associating a subset of features from the feature set with entities having relationship types and corresponding to pages on a social networking system; and reducing the feature set based on at least one rule applied to the relationship types. 12. The system of claim 11, wherein application of the at least one rule to the relationship types is subject to a condition based on interactions with the pages. 13. The system of claim 12, further comprising: determining counts of the interactions with the pages. 14. The system of claim 13, wherein the relationship types include a relationship involving a child entity having a parent entity. 15. The system of claim 14, wherein the at least one rule provides that the child entity is selected as a feature for the child entity when the condition is satisfied and wherein the condition requires that the selected child entity have a count of interactions that is a predetermined factor higher than a count of interactions of the parent entity. 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 feature set for a model to be trained by machine learning; associating a subset of features from the feature set with entities having relationship types and corresponding to pages on a social networking system; and reducing the feature set based on at least one rule applied to the relationship types. 17. The non-transitory computer-readable storage medium of claim 16, wherein application of the at least one rule to the relationship types is subject to a condition based on interactions with the pages. 18. The non-transitory computer-readable storage medium of claim 17, further comprising: determining counts of the interactions with the pages. 19. The non-transitory computer-readable storage medium of claim 18, wherein the relationship types include a relationship involving a child entity having a parent entity. 20. The non-transitory computer-readable storage medium of claim 19, wherein the at least one rule provides that the child entity is selected as a feature for the child entity when the condition is satisfied and wherein the condition requires that the selected child entity have a count of interactions that is a predetermined factor higher than a count of interactions of the parent entity.