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 can determine at least one web site that is of interest to a user of the social networking system. One or more pages can be determined based at least in part on the web site, the one or more pages being accessible through the social networking system. At least one page recommendation that references at least one of the one or more pages can be provided to the user.

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: A social networking system selects a set of groups for presentation to a user of the social networking system. To select groups, the social networking system determining scores for various groups representing a likelihood of the user interacting with the groups. When determining a score for a group, the social networking system accounts for a likelihood of the user providing content to the group as well as the user accessing or viewing content associated with the group. Based on the scores, one or more groups are selected and presented to the user. Additionally, the social networking system may apply one or more diversity rules so that the selected groups have a variety of characteristics.

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: 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 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: A social networking system selects a set of groups for presentation to a user of the social networking system. To select groups, the social networking system determining scores for various groups representing a likelihood of the user interacting with the groups. When determining a score for a group, the social networking system accounts for a likelihood of the user providing content to the group as well as the user accessing or viewing content associated with the group. Based on the scores, one or more groups are selected and presented to the user. Additionally, the social networking system may apply one or more diversity rules so that the selected groups have a variety of characteristics.

Abstract: A social networking system selects a set of groups for presentation to a user of the social networking system. To select groups, the social networking system determines scores for various groups representing a likelihood of the user interacting with the groups. The social networking system may identify a set of groups based on interactions between the user and various groups occurring during a specific time interval and determine scores for groups in the set. When determining a score for a group, the social networking system accounts for times associated with interactions between the user and various groups. Based on the scores, one or more groups are selected and presented to the user. Additionally, the social networking system may identify a time to present the selected one or more groups to the user based on prior user interactions with various groups.

Abstract: A social networking system selects a set of groups for presentation to a user of the social networking system. To select groups, the social networking system identifies candidate groups and selects the set of groups from the candidate groups. To identify certain candidate groups, the social networking system determines a location associated with various groups based on locations associated with users included in the group. For example, the social networking system determines a centroid of a group based on locations associated with users included in the group and associates the centroid with the group if at least a threshold percentage of distances between locations associated with users included in the group and the centroid do not exceed a threshold distance. Groups associated with locations within a threshold distance of a location associated with the user are identified as candidate groups.

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 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: 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 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 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 selects a set of groups for presentation to a user of the social networking system. To select groups, the social networking system determining scores for various groups representing a likelihood of the user interacting with the groups. When determining a score for a group, the social networking system accounts for a likelihood of the user providing content to the group as well as the user accessing or viewing content associated with the group. Based on the scores, one or more groups are selected and presented to the user. Additionally, the social networking system may apply one or more diversity rules so that the selected groups have a variety of characteristics.

Abstract: A social networking system selects a set of groups for presentation to a user of the social networking system. To select groups, the social networking system determining scores for various groups representing a likelihood of the user interacting with the groups. When determining a score for a group, the social networking system accounts for a likelihood of the user providing content to the group as well as the user accessing or viewing content associated with the group. Based on the scores, one or more groups are selected and presented to the user. Additionally, the social networking system may apply one or more diversity rules so that the selected groups have a variety of characteristics.

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: Technology for media item and user language classification is disclosed. Media item classification may use models for associating language identifiers or probability distributions for multiple languages with linguistic content. User language classification may define user language models for attributing to users indications of languages they speak read, and/or write. The text classifications and user classifications may interact because the probability that given text is in a particular language may depend on a determined likelihood the user who produced the text speaks that language, or conversely, a user interacting with text in a particular language may increase the likelihood they understand that language. Some embodiments use language-tagged social media content to train n-gram classifiers for use with other social media content.

Abstract: A system identifies unconnected content items of high quality and provides the unconnected content items for display to a user. The method comprises receiving several content items posted on pages of a social networking system. The system then determines a subset of those content items (e.g., high quality content items). A topic is then extracted from each of the subset of content items. The topic is mapped to one or more related pages of the social networking system that represent an expanded set of pages associated with the content item. For each of the related pages, a user is identified who is connected to the related page. Finally, the content item (e.g., a high quality content item) is provided to the user for display in the user's newsfeed.