Systems, methods, and non-transitory computer-readable media can train a machine learning model to output respective scores for content items based at least in part on information describing a user, wherein a score for a content item measures a likelihood that the user will select the content item to be included in a social profile of the user. A determination is made that a first user of the social networking system is eligible for a content item suggestion. A first content item to be provided as a suggestion to the first user is determined based at least in part on the model. The first content item is provided as a suggestion to the first user for use in a social profile of the first user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: training, by a social networking system, a machine learning model to output respective scores for content items based at least in part on information describing a user, wherein a score for a content item measures a likelihood that the user will select the content item to be included in a social profile of the user; determining, by the social networking system, that a first user of the social networking system is eligible for a content item suggestion; determining, by the social networking system, a first content item to be provided as a suggestion to the first user based at least in part on the model; and providing, by the social networking system, the first content item as a suggestion to the first user for use in a social profile of the first user. 2. The computer-implemented method of claim 1, wherein determining that the first user of the social networking system is eligible for a content item suggestion further comprises: determining, by the social networking system, that the first user has not selected a cover photo for use in the social profile of the first user. 3. The computer-implemented method of claim 1, wherein determining that the first user of the social networking system is eligible for a content item suggestion further comprises: determining, by the social networking system, that the first user has uploaded a content item to the social networking system. 4. The computer-implemented method of claim 1, wherein training the model further comprises: generating, by the social networking system, a set of training examples that each include information describing a user and a corresponding set of features that describe a cover photo being used in a social profile of the user. 5. The computer-implemented method of claim 4, wherein generating the set of training examples further comprises: determining, by the social networking system, one or more sets of words that describe a user from content that was published by the user through the social networking system. 6. The computer-implemented method of claim 5, wherein determining the one or more sets of words further comprises: extracting, by the social networking system, at least one noun, verb, or concept from the content that was published by the user. 7. The computer-implemented method of claim 4, wherein generating the set of training examples further comprises: determining, by the social networking system, one or more sets of words that describe a user from images that are associated with the user in the social networking system. 8. The computer-implemented method of claim 1, wherein determining the first content item to be provided as a suggestion to the first user further comprises: determining, by the social networking system, respective scores for a set of content items based at least in part on the model, wherein the set of content items includes the first content item; and determining, by the social networking system, that the first content item has a highest score among the set of content items. 9. The computer-implemented method of claim 8, wherein the scores are adjusted based at least in part on a temporal degradation function. 10. The computer-implemented method of claim 8, wherein the set of content items includes content items that were uploaded to the social networking system by the first user. 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: training a machine learning model to output respective scores for content items based at least in part on information describing a user, wherein a score for a content item measures a likelihood that the user will select the content item to be included in a social profile of the user; determining that a first user of the social networking system is eligible for a content item suggestion; determining a first content item to be provided as a suggestion to the first user based at least in part on the model; and providing the first content item as a suggestion to the first user for use in a social profile of the first user. 12. The system of claim 11, wherein determining that the first user of the social networking system is eligible for a content item suggestion further causes the system to perform: determining that the first user has not selected a cover photo for use in the social profile of the first user. 13. The system of claim 11, wherein determining that the first user of the social networking system is eligible for a content item suggestion further causes the system to perform: determining that the first user has uploaded a content item to the social networking system. 14. The system of claim 11, wherein training the model further causes the system to perform: generating a set of training examples that each include information describing a user and a corresponding set of features that describe a cover photo being used in a social profile of the user. 15. The system of claim 14, wherein generating the set of training examples further causes the system to perform: determining one or more sets of words that describe a user from content that was published by the user through the social networking system. 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: training a machine learning model to output respective scores for content items based at least in part on information describing a user, wherein a score for a content item measures a likelihood that the user will select the content item to be included in a social profile of the user; determining that a first user of the social networking system is eligible for a content item suggestion; determining a first content item to be provided as a suggestion to the first user based at least in part on the model; and providing the first content item as a suggestion to the first user for use in a social profile of the first user. 17. The non-transitory computer-readable storage medium of claim 16, wherein determining that the first user of the social networking system is eligible for a content item suggestion further causes the computing system to perform: determining that the first user has not selected a cover photo for use in the social profile of the first user. 18. The non-transitory computer-readable storage medium of claim 16, wherein determining that the first user of the social networking system is eligible for a content item suggestion further causes the computing system to perform: determining that the first user has uploaded a content item to the social networking system. 19. The non-transitory computer-readable storage medium of claim 16, wherein training the model further causes the computing system to perform: generating a set of training examples that each include information describing a user and a corresponding set of features that describe a cover photo being used in a social profile of the user. 20. The non-transitory computer-readable storage medium of claim 16, wherein generating the set of training examples further causes the computing system to perform: determining one or more sets of words that describe a user from content that was published by the user through the social networking system.