Abstract:
Systems, methods, and non-transitory computer-readable media can determine a respective embedding for each entity in a set of entities that are accessible through the social networking system, wherein each embedding is learned based at least in part on one or more sessions of connections formed between users and entities of the social networking system. One or more candidate entities that are related to a first entity can be determined based at least in part on the respective embeddings 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.
Abstract:
Systems, methods, and non-transitory computer readable media configured to determine seed content items based on interests of a user. Candidate content items can be determined for potential presentation to the user based at least in part on the seed content items. Features associated with the candidate content items can be processed to generate probabilities that the user will perform interactions with the candidate content items. Values can be assigned to the candidate content items based on the probabilities that the user will perform interactions with the candidate content items and the importance of the interactions. The values can be provided as bid values to an auction system to determine constraints regarding presentation of the candidate content items. Presentation of the candidate content items can be optimized.
Abstract:
Systems, methods, and non-transitory computer readable media configured to determine whether a candidate content item may be presented in response to an indication of approval by a user regarding a seed content item according to a first technique. It is determined whether the seed content item may be presented in response to an indication of approval by the user regarding the candidate content item according to a second technique. Features, including a reciprocity feature based on the determining whether a candidate item may be presented and the determining whether the seed content item may be presented, are processed to generate a probability that the user will interact with the candidate content item.
Abstract:
Particular embodiments detect events associated with information about events and activities that a user has engaged in. The events may be of a particular type. An entity associated with an event may request that the user provide further information on the event and, based on the received information, the social-networking system sends the user a request for follow-up information after an appropriate time delay. The time delay may vary based on the user activity and the type or context of the event that triggered the request. After the follow-up information is received, such information is stored in the social-networking system and may be used to determine recommendations, sponsored stories, advertisements, etc. to send to friends of the user. The information may also be used for ranking or filtering recommendations.
Abstract:
Systems, methods, and non-transitory computer-readable media can determine respective geographic locations of a set of users associated with a page that is accessible through a social network. At least one centroid for the page can be generated based at least in part on the respective geographic locations of the set of users. At least one area of influence of the page can be determined based at least in part on the centroid. At least one page recommendation can be presented to one or more users in the set of users based at least in part on the area of influence of the
Abstract:
Systems, methods, and non-transitory computer-readable media can determine a respective embedding for each entity in a set of entities that are accessible through the social networking system, wherein each embedding is learned based at least in part on one or more sessions of connections formed between users and entities of the social networking system. One or more candidate entities that are related to a first entity can be determined based at least in part on the respective embeddings 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.
Abstract:
Systems, methods, and non-transitory computer readable media configured to determine features based on online user behavior regarding a seed content item and a candidate content item that may be presented in response to an indication of approval by a user regarding the seed content item. The features are processed to generate a probability that the user will interact with the candidate content item. The candidate content item is selected for presentation to the user based on the probability that the user will interact with the candidate content item.
Abstract:
Particular embodiments detect events associated with information about activities that a user has engaged in. The activities may be associated with a location or location-agnostic. Based on the received information, the social-networking system sends the user a request for follow-up information after an appropriate time delay. The time delay may vary based on the user activity and the context of the event that triggered the request. After the follow-up information is received, such information is stored in the social-networking system and may be used to determine recommendations, sponsored stories, advertisements, etc. to send to friends of the user. The information may also be used for ranking or filtering recommendations.
Abstract:
Particular embodiments detect events associated with information about activities that a user has engaged in. The activities may be associated with a location or location-agnostic. Based on the received information, the social-networking system sends the user a request for follow-up information after an appropriate time delay. The time delay may vary based on the user activity and the context of the event that triggered the request. After the follow-up information is received, such information is stored in the social-networking system and may be used to determine recommendations, sponsored stories, advertisements, etc. to send to friends of the user. The information may also be used for ranking or filtering recommendations.
Abstract:
Particular embodiments detect an indication of an event that is initiated on an online social network by a user and that is related to an entity associated with an activity that the user has engaged in. Based on the received indication, a social-networking system sends the user a request for follow-up information about the activity. After the follow-up information is received, the social-networking system retrieves contacts of the user and identifies contacts that have engaged in social activities on the online social network related to the entity. Recommendations for the entity are then sent to those identified contacts.