Abstract: In one embodiment, a method includes accessing for a user one or more electronic notifications that include information about a social network. The social network includes a plurality of nodes connected by a plurality of edges, with at least one node corresponding to the user. The method further includes determining, for each of the electronic notifications, a score that estimates whether a user interaction with the social network will result if the electronic notification is provided to the user. The method further includes determining, based at least in part on the determined scores, an action to take with respect to the electronic notifications.

Abstract: Embodiments are disclosed for data computation of collaborative filtering in a social network. Collaborative filtering involves predicting a user's behavior or interests based on other users' behavior or interests. To predict a user's interests in an item such as a picture, a system performs an iterative computation to perform an evaluation by solving an objective function. The system characterizes “users” as “vertices” in a directed graph, “relationship among users” as “edges” in the directed graph, and “items” as “worker data” that is locally-calculated, stored, and managed in individual worker computers. When a local computing process is completed, the “worker data” can be transferred to other worker computers so as to complete a whole computing process. The system enhances an overall computing efficiency and enables collaborative filtering across a large data set.

Abstract: The disclosure is directed to evaluating feature vectors using decision trees. Typically, the number of feature vectors and the number of decision trees are very high, which prevents loading them into a processor cache. The feature vectors are evaluated by processing the feature vectors across a disjoint subset of trees repeatedly. After loading the feature vectors into the cache, they are evaluated across a first subset of trees, then across a second subset of trees and so on. If the values based on the first and second subsets satisfy a specified criterion, further evaluation of the feature vectors across the remaining of the decision trees is terminated, thereby minimizing the number of trees evaluated and therefore, consumption of computing resources.

Abstract: In one embodiment, a method includes accessing for a user one or more electronic notifications that include information about a social network. The social network includes a plurality of nodes connected by a plurality of edges, with at least one node corresponding to the user. The method further includes determining, for each of the electronic notifications, a score that estimates whether a user interaction with the social network will result if the electronic notification is provided to the user. The method further includes determining, based at least in part on the determined scores, an action to take with respect to the electronic notifications.

Abstract: In one embodiment, a method includes one or more computing devices accessing a notification to be sent to a user, where the notification has a context. The method also includes one or more computing devices sending a request to a history service for historical notification data associated with the user with respect to the context of the notification and a ranking of the notification where the ranking indicates a probability of the user interacting with the notification. The method also includes one or more computing devices receiving the historical notification data and the ranking from the history service. Moreover, the method also includes one or more computing devices determining a notification policy to apply to the notification based at least in part on the context of the notification, the historical notification data, and the ranking. Furthermore, the method also includes one or more computing devices applying the notification policy to the notification to be sent to the user.

Abstract: In one embodiment, a method includes receiving first-user input corresponding to a check-in for the first user, accessing a check-in history of the first user, and identifying one or more second users based at least in part on the check-in history, where the first user is a user of a social-networking system that includes a number of nodes and a number of edges connecting the nodes, with at least one node corresponding to the first user. The method includes providing an identification of one or more of the identified second users for display to the first user in association with the check-in.

Abstract: Online systems generate predictors for predicting actions of users of the online system. The online system receives requests to generate predictor models for predicting whether a user is likely to take an action of a particular action type. The request specifies the type of action and criteria for identifying a successful instance of the action type and a failure instance of the action type. The online system collects data including successful and failure instances of the action type. The online system generates one or more predictors of different types using the generated data. The online system evaluates and compares the performance of the different predictors generated and selects a predictor based on the performance. The online system returns a handle to access the generated predictor to the requester of the predictor.

Abstract: A social networking system selects a set of contacts for presentation to a user of the social networking system. A contact is an additional user or a group of users maintained by the social networking system. To select the set of contacts, the social networking system generates scores associated with additional users of the social networking system and groups of users based on interactions between the user and various additional users and between the user and groups of users. Based on the scores associated with additional users and with groups of users, the social networking system selects a set of contacts from the additional users and the groups of users. Information identifying the set of contacts is communicated from the social networking system to a client device for presentation to the user.

Abstract: In one embodiment, a method includes accessing for a user one or more electronic notifications that include information about a social network. The social network includes a plurality of nodes connected by a plurality of edges, with at least one node corresponding to the user. The method further includes determining, for each of the electronic notifications, a score that estimates whether a user interaction with the social network will result if the electronic notification is provided to the user. The method further includes determining, based at least in part on the determined scores, an action to take with respect to the electronic notifications.

Abstract: Online systems generate predictors for predicting actions of users of the online system. The online system receives requests to generate predictor models for predicting whether a user is likely to take an action of a particular action type. The request specifies the type of action and criteria for identifying a successful instance of the action type and a failure instance of the action type. The online system collects data including successful and failure instances of the action type. The online system generates one or more predictors of different types using the generated data. The online system evaluates and compares the performance of the different predictors generated and selects a predictor based on the performance. The online system returns a handle to access the generated predictor to the requester of the predictor.

Abstract: Members of a social network user's social graph are automatically segregated into overlapping clusters according to patterns of their past communications. Each cluster within the social graph represents a group of members having a high degree of intra-cluster communication or other connection with one another. The clustering is performed according to a sorting or ranking in accordance with non-principal eigenvectors of connectivity matrices describing the intra-cluster communications/connections. The overlapping clusters exhibit maximum internal density and minimum external sparsity.

Abstract: Members of a social network user's social graph are automatically segregated into overlapping clusters according to patterns of their past communications. Each cluster within the social graph represents a group of members having a high degree of intra-cluster communication or other connection with one another. The clustering is performed according to a sorting or ranking in accordance with non-principal eigenvectors of connectivity matrices describing the intra-cluster communications/connections. The overlapping clusters exhibit maximum internal density and minimum external sparsity.

Abstract: As the result of a keyword search, real time and social news stream Web search results are retrieved and analyzed to build a topic model of n-grams. The n-grams of the topic model are treated as ad-based keywords to determine advertisements to be displayed in conjunction with the real time Web search results. The real time Web search results and the advertisements are then be presented or displayed for user consumption or review.