Abstract:
In one embodiment, a computing system may access a social graph of an online social network comprising a plurality of nodes and a plurality of edges connecting the nodes, where each of the edges between two of the nodes represent a single degree of separation between them, and the nodes comprise a first node corresponding to a first user of the online social network, and a plurality of second nodes corresponding to a plurality of second users associated with the online social network. The computing system may calculate a content score for each of one or more content items shared by the second users on the online social network, where each content score is based at least in part on a proximity coefficient between the first user and the content item. The computing system may send one or more of the scored content items for display to the first user.
Abstract:
User profile information for a user of a social networking system is inferred based on information about user profile of the user's connections in the social networking system. The inferred user profile attributes may include age, gender, education, affiliations, location, and the like. To infer a value of a user profile attribute, the system may determine an aggregate value based on the attributes of the user's connections. A confidence score may also be associated with the inferred attribute value. The set of connections analyzed to infer a user profile attribute may depend on the attribute, the types of connections, and the interactions between the user and the connections. The inferred attribute values may be used to update the user's profile and to determine information relevant to the user to be presented to the user (e.g., targeting advertisements to the user based on the user's inferred attributes).
Abstract:
In one embodiment, a computing system may access a social graph of an online social network comprising a plurality of nodes and a plurality of edges connecting the nodes, where each of the edges between two of the nodes represent a single degree of separation between them, and the nodes comprise a first node corresponding to a first user of the online social network, and a plurality of second nodes corresponding to a plurality of second users associated with the online social network. The computing system may calculate a content score for each of one or more content items shared by the second users on the online social network, where each content score is based at least in part on a proximity coefficient between the first user and the content item. The computing system may send one or more of the scored content items for display to the first user.
Abstract:
In one embodiment, a computing system may access a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the nodes comprising a first node corresponding to the first user, the first user being associated with an online social network, and a number of second nodes corresponding to a number of second users associated with the online social network. The computing system may receive an indication of a first location of a mobile-client system of the first user. The computing system may identify one or more second users based on one or more notification rules, where each second user is associated with a mobile-client system having a second location within a threshold distance of the first location.
Abstract:
In one embodiment, one or more computing systems receive a request for a location prediction for a user from a service. The computing systems access one or more real-time location signals and one or more aggregated location signals. The aggregated location signals may comprise one or more previous location signals. The computing systems may then generate one or more location predictions from the one or more real-time location signals and the one or more aggregated location signals, and calculate a single location prediction for the user from the one or more location predictions. The computing systems may then send, in response to the request, the single location prediction for the user to the requesting service.
Abstract:
In one embodiment, one or more computing systems receive a request for a location prediction for a user from a service. The computing systems access one or more real-time location signals and one or more aggregated location signals. The aggregated location signals may comprise one or more previous location signals. The computing systems may then generate one or more location predictions from the one or more real-time location signals and the one or more aggregated location signals, and calculate a single location prediction for the user from the one or more location predictions. The computing systems may then send, in response to the request, the single location prediction for the user to the requesting service.
Abstract:
In one embodiment, a mobile client system may determine its location. The mobile client system may store the location in a location history in a memory of the mobile client system, where the location history comprises one or more geographic locations and one or more time stamps corresponding to each of the geographic locations. The mobile client system may detect its current status based at least in part on whether the mobile client system is stationary. The mobile client system may send the location history to a location server of an online social network based at least in part on the current status of the mobile client system and a power requirement for sending the location history to the location server.
Abstract:
Techniques to manage client location detection are described. In one embodiment an apparatus may comprise a location-based services support component and a client management component. The location-based services support component may be operative to determine that a network service is scheduled for a location update from a mobile device, determine a location accuracy based on the network service, and update the network service with a received location of the mobile device. The client management component may be operative to transmit a location request to the mobile device, the location request specifying the location accuracy determined based on the network service and receive a response to the location request from the mobile device, the response comprising the location of the mobile device conforming to the specified location accuracy. Other embodiments are described and claimed.
Abstract:
In one embodiment, a computing system may access a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, where the nodes comprise a first node corresponding to a first user of an online social network, and a plurality of second nodes that each correspond to a concept or a second user associated with the online social network. The computing system may receive a search query from the first user. The computing system may generate one or more search results corresponding to the search query, where each search result corresponds to a node of the plurality of second nodes. The computing system may score each search result based on a proximity coefficient between the first node and the node corresponding to the search result.
Abstract:
Particular embodiments of a social-networking system maintain one or more data stores storing a social graph comprising user nodes, concept nodes, and edges connecting the nodes. Particular embodiments may determine a confidence score with respect to a user node and a concept node, wherein the confidence score is based at least in part on affinity scores associated with the edges along a sequence of nodes between the user node and the concept node in the social graph. The confidence score may be based on an overall probability that a random walk starting at the user node will end at the concept node. This overall probability may be determined by calculating, for each edge in the random walk, the probability of taking that edge during the random walk, based on the affinity score associated with that edge.