Abstract: Systems, methods, and non-transitory computer readable media are configured to apply a spectral clustering technique to at least a portion of a similarity graph to generate clusters of geographic sub-regions constituting geographic regions. A tf-idf technique is performed to determine pages of a social networking system associated with a geographic region as potential local suggestions for a user associated with a geographic sub-region in the geographic region. References to at least a portion of the pages are presented as local suggestions to the user.

Abstract: Systems, methods, and non-transitory computer-readable media can select a set of selected pages from a plurality of pages on a social networking system based on page selection criteria. A set of potential stories from the set of selected pages is aggregated. The set of potential stories are ranked based on ranking criteria. An administrator feed associated with a first page is generated, the administrator feed comprising a plurality of stories from the set of potential stories based on the ranking the set of potential stories.

Abstract: In one embodiment, a method includes receiving a query input, parsing the query input to identify one or more n-grams, determining a search bias of the first user with respect to the query input, the search bias being determined based on an explicit bias and an implicit bias of the first user, wherein the explicit bias is based on an analysis of the entities associated with the online social networking matching n-grams in the query input, and wherein the implicit bias is based on an analysis of user-profile information of a plurality of second users sharing one or more user attributes with the first user, identifying content objects matching the query input based at least in part on the search bias of the first user, and sending instructions for presenting a search-results interface comprising references to the identified content objects.

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: 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: In one embodiment, a method includes receiving a query, identifying one or more nodes of a plurality of second nodes corresponding to the query, calculating a score for each of the identified nodes using a probabilistic ranking model that scores each node based at least in part on a number of edges connecting the node to one or more nodes within a first set of user nodes that includes the first node and user nodes corresponding to second users sharing one or more user attributes with the first user, and generating corresponding search results. The score calculated for each of the identified nodes may bias the search results toward nodes connected to disproportionately more nodes in the first set of user nodes than nodes in the plurality of second nodes that correspond to an overall population of users of the online social network.

Abstract: A social networking system recommends pages or other objects to a user with which the user may establish a connection to receive content associated with an object. Candidate objects may be identified by the social networking system as objects connected to additional users who are connected to an object connected to the user. To recommend objects with which the user is likely to have an interest, the social networking system extracts one or more topics from the object connected to the user and from various candidate objects. Based on a topic graph, the social networking system determines measures of relatedness between topics extracted from various candidate objects and an object connected to the user. The measures of relatedness are then used to select one or more of the candidate objects to identify to the user.

Abstract: Systems, methods, and non-transitory computer readable media are configured to apply a spectral clustering technique to at least a portion of a similarity graph to generate clusters of geographic sub-regions constituting geographic regions. A tf-idf technique is performed to determine pages of a social networking system associated with a geographic region as potential local suggestions for a user associated with a geographic sub-region in the geographic region. References to at least a portion of the pages are presented as local suggestions to the user.

Abstract: Systems, methods, and non-transitory computer-readable media can select a set of selected pages from a plurality of pages on a social networking system based on page selection criteria. A set of potential stories from the set of selected pages is aggregated. The set of potential stories are ranked based on ranking criteria. An administrator feed associated with a first page is generated, the administrator feed comprising a plurality of stories from the set of potential stories based on the ranking the set of potential stories.

Abstract: In one embodiment, a method includes receiving a query input, parsing the query input to identify one or more n-grams, determining a search bias of the first user with respect to the query input, the search bias being determined based on an explicit bias and an implicit bias of the first user, wherein the explicit bias is based on an analysis of the entities associated with the online social networking matching n-grams in the query input, and wherein the implicit bias is based on an analysis of user-profile information of a plurality of second users sharing one or more user attributes with the first user, identifying content objects matching the query input based at least in part on the search bias of the first user, and sending instructions for presenting a search-results interface comprising references to the identified content objects.

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 can determine a plurality of candidate entities for recommendation to a user of a social networking system based on candidate criteria. A recommendation pace rating is determined for each of the plurality of candidate entities based on historical recommendation data. A first entity of the plurality of candidate entities is selected for recommendation to the user based on the recommendation pace ratings.

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 determine one or more geographic clusters that each correspond to a respective portion of a geographic region, each geographic cluster representing a neighborhood that includes a set of places which users residing in the neighborhood tend to frequently visit. A determination can be made that a user is located in a first geographic cluster. At least one content item can be provided to be presented to the user, the content item being associated with the first geographic cluster.

Abstract: In one embodiment, a method includes receiving a query, determining a user bias of a first user of an online social network from a first node corresponding to the first user and a plurality of user nodes corresponding to a plurality of second users sharing one or more user attributes with the first user, identifying nodes of a plurality of second nodes based at least in part on the user bias of the first user, where the identified nodes correspond to the structured query, and generating search results corresponding to the identified nodes. The bias may be determined by identifying a candidate user node of the second nodes, comparing a first user attribute of the first node to a second user attribute of the candidate user node, and including the candidate user node in the user nodes when the first user attribute matches the second user attribute.

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: A social networking system recommends pages or other objects to a user with which the user may establish a connection to receive content associated with an object. Candidate objects may be identified by the social networking system as objects connected to additional users who are connected to an object connected to the user. To recommend objects with which the user is likely to have an interest, the social networking system extracts one or more topics from the object connected to the user and from various candidate objects. Based on a topic graph, the social networking system determines measures of relatedness between topics extracted from various candidate objects and an object connected to the user. The measures of relatedness are then used to select one or more of the candidate objects to identify to the user.

Abstract: In one embodiment, a method includes receiving a query, identifying one or more nodes of a plurality of second nodes corresponding to the query, calculating a score for each of the identified nodes using a probabilistic ranking model that scores each node based at least in part on a number of edges connecting the node to one or more nodes within a first set of user nodes that includes the first node and user nodes corresponding to second users sharing one or more user attributes with the first user, and generating corresponding search results. The score calculated for each of the identified nodes may bias the search results toward nodes connected to disproportionately more nodes in the first set of user nodes than nodes in the plurality of second nodes that correspond to an overall population of users of the online social network.

Abstract: In one embodiment, a method includes receiving a query, determining a user bias of a first user of an online social network from a first node corresponding to the first user and a plurality of user nodes corresponding to a plurality of second users sharing one or more user attributes with the first user, identifying nodes of a plurality of second nodes based at least in part on the user bias of the first user, where the identified nodes correspond to the structured query, and generating search results corresponding to the identified nodes. The bias may be determined by identifying a candidate user node of the second nodes, comparing a first user attribute of the first node to a second user attribute of the candidate user node, and including the candidate user node in the user nodes when the first user attribute matches the second user attribute.