Abstract:
The social networking system monitors implicit interactions between a user and objects of the social networking system with which the user has not established a connection. Based on the implicit interactions between the user and an object, the social networking system identifies a soft connection between the user and the object. The social networking system may then identify soft connections to include in a candidate list of soft connections to recommend to the user. The social networking system may also extract signals from the set of candidate list of soft connections, and may use the extracted signals to rank the soft connections in the list of candidate soft connections. The social networking system may then recommend soft connections to the user based on the rank associated with the soft connections in the candidate list of soft connections.
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:
A social networking system recommends objects, such as pages, of the social networking system to users of the social networking system based on the location of the user. The social networking system obtains location information identifying the location of the user. Based on the location of the user, the social networking system identifies levels of geographical partitions encompassing the location of the user. For each level of geographical partitions, the social networking system accesses relevant objects of the social networking system with connections to users located within the level of geographical partitions. The social networking system may have determined a term frequency-inverse document frequency (tf-idf) value for each relevant object. Based on the number of connections and the tf-idf value associated with each relevant object, the social networking system merges the relevant objects accessed at each level into a set of relevant objects to recommend to the user.
Abstract:
Systems, methods, and non-transitory computer readable media are configured to generate multiple channel embeddings for a page of a social networking system. The multiple channel embeddings can be mapped to a shared embedding space. A page embedding for the page of the social networking system can then be generated.
Abstract:
An online system maintains a web page associated with one or more page administrators. The online system trains a machine learning model to determine a likelihood of a page administrator account accepting a request for the online system to present content about the web page to other users of the online system. The model uses features extracted from data about the page administrator accounts on the online system, the page administrator interactions with the online system, and the web page. The online system selects one or more page administrator accounts and sends them requests based on the determined likelihood scores. The online system delivers content associated with the web page to users of the online system based on a response to the request.
Abstract:
Disclosed is a method for supporting online services in providing content items to users of an online system. A request for loading a webpage of an online system is received from a client device. A plurality of content items eligible for being presented to the user is received. For each of the received content items, a trained model is identified based on characteristics of the content item, and a score is determined using the identified trained model and based on characteristics of the user. One or more content items is selected based on the determined scores. The selected one or more content items are sent to the client device for presentation to the user.
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 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:
Systems, methods, and non-transitory computer-readable media can determine a profile model for a page that is accessible through the social networking system, the profile model describing one or more modal characteristics of users of the social networking system that have fanned the page. A determination can be made that the page should be recommended to a first user of the social networking system based at least in part on the profile model. At least one page recommendation that references the page can be provided to the first user.
Abstract:
An online system maintains a web page associated with one or more page administrators. The online system trains a machine learning model to determine a likelihood of a page administrator account accepting a request for the online system to present content about the web page to other users of the online system. The model uses features extracted from data about the page administrator accounts on the online system, the page administrator interactions with the online system, and the web page. The online system selects one or more page administrator accounts and sends them requests based on the determined likelihood scores. The online system delivers content associated with the web page to users of the online system based on a response to the request.