Abstract: Systems, methods, and non-transitory computer-readable media can present a service directory landing page comprising a plurality of selectable service category options associated with a plurality of pre-defined service categories. A search results page is presented, including one or more search results based on search criteria. Each of the one or more search results is associated with an entity page of a social networking system. The service directory landing page and the search results page are accessible without logging into the social networking system. Each entity page on the social networking system is accessible only when logged into the social networking system.

Abstract: Systems, methods, and non-transitory computer-readable media can receive JOINKEY information identifying a JOINKEY and embedding element information identifying a plurality of embedding elements associated with the JOINKEY. A training instance is created comprising a pre-determined number of embedding elements from the plurality of embedding elements. A plurality of negative samples from a sample cache are added to the training instance. One or more embeddings are updated based on the training instance.

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: 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: 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 can determine a plurality of candidate entities for recommendation to a user of a social networking system. A predicted activity objective value model configured to calculate activity stores for candidate entities is established. The activity score is indicative of the probability of future activity on the social networking system by a candidate entity. A first activity score is determined for each of the plurality of candidate entities based on the predicted activity object value model and a first set of feature values. A second activity score is determined for each of the plurality of candidate entities based on the predicted activity object value model and a second set of feature values that is different from the first set of feature values. A first entity is selected of the plurality of candidate entities based on the first and second activity scores.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a change made to a page that is accessible through a social networking system. A page update story that describes the change can be generated. The page update story to be published through the social networking system.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a frequency by which content items published by an entity are to be provided for a user. Examples of content items previously published by the entity are selected. Information is provided to assist the user in a determination about whether to assent to provision of content items published by the entity for the user.

Abstract: 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.

Abstract: Systems, methods, and non-transitory computer-readable media can generate layered training data for determining embeddings for entities that are accessible through the social networking system, wherein the layered training data includes layers of data that are organized by a hierarchy, and wherein each layer of data corresponds to entities of a same type. A respective embedding for each entity in a set of entities can be determined, wherein the embeddings are trained iteratively using each layer of data in the layered training data. 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 are configured to determine an interaction between a first entity and a first item. A second entity can be determined. The first entity can have formed a connection with the second entity on a social networking system. A belief that the second entity will interact with the first item can then be generated.

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 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 are configured to generate, an embedding for a post. The post can correspond to an entity. An embedding for a comment in a set of comments can be generated. The comments in the set can be responsive to the post. The embedding for the post can be updated. The updating can be based on the embedding for the post and the embedding for the comment. Subsequently, a rank for the comment in the set of comments can be determined.

Abstract: Systems, methods, and non-transitory computer readable media configured to generate session information based on information regarding items of a plurality of item types associated with interactions performed by active users of a social networking system. A graph is generated based on the session information. At least a first item of the items is assigned to a cluster based on similarity between the item and the cluster. The cluster is provided to a recommender system to facilitate selection of relevant information for potential presentation to a user.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a frequency by which content items published by an entity are to be provided for a user. Examples of content items previously published by the entity are selected. Information is provided to assist the user in a determination about whether to assent to provision of content items published by the entity for the user.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a user query comprising one or more search terms. One or more synonyms are identified for the user query based on a dynamic thesaurus generated using automated synonym extraction. An expanded query is generated based on the user query and the one or more synonyms. One or more search results are identified based on the expanded query.

Abstract: An online system, such as a social networking system, recommends pages of content to users. The recommendation is presented in a recommendation unit presenting one or more representations of pages to a user. Additionally, the user may interact with the recommendation unit to change representations of pages presented by the recommendation unit. A representation of a page presented by the recommendation unit includes content from one or more content items on the page selected based on interaction with the content items on the page and types of content included in content items on the page (e.g., image data, video data, destination address). Representations of different pages may differ based on the types of content included in content items selected from the different pages.

Abstract: Systems, methods, and non-transitory computer readable media configured to generate session information based on information regarding items of a plurality of item types associated with interactions performed by active users of a social networking system. A graph is generated based on the session information. At least a first item of the items is assigned to a cluster based on similarity between the item and the cluster. The cluster is provided to a recommender system to facilitate selection of relevant information for potential presentation to a user.

Abstract: Systems, methods, and non-transitory computer readable media can obtain a plurality of page engagement graphs, each of the plurality of page engagement graphs associated with a page engagement type of a plurality of page engagement types. Respective weights associated with the plurality of page engagement types can be determined. An aggregated page engagement graph can be generated based on the plurality of page engagement graphs and the respective weights. Pages in the aggregated page engagement graph can be ranked.