Abstract: An information processing apparatus acquires user features indicating features for each of a plurality of users and item features indicating features for each of a plurality of items, acquires time-series information indicating time-series interactions between the plurality of users and the plurality of items, constructs, based on the user features, the item features, and the time-series information, a graph including a plurality of user nodes representing the plurality of users, a plurality of item nodes representing the plurality of items, and links indicating interaction relationships in the plurality of user nodes and the plurality of item nodes, and extracts, from the graph, a node representation in the graph for any node among the plurality of user nodes and the plurality of item nodes.

Abstract: An information processing apparatus acquires factual features of each of a plurality of users as user features, creates a relationship graph indicating a social relationship among the plurality of users based on the user features, sets a target user among the plurality of users, and predicts a disease risk indicating the risk of developing at least one predetermined disease for the target user, based on the relationship graph and the user feature of the target user.

Abstract: An information processing apparatus selects, from a plurality of users, a model user group and a target user group that is different from the model user group, generates, for each user of the target user group, a user representation representing relationships with the plurality of users and the plurality of items, and predicts, based on the user representation, one or more users that have features similar to those of users included in the model user group, from the target user group, as a similar user group.

Abstract: Provided is a technique for realizing targeting in which social connections are considered. An information processing apparatus acquires factual features of each of a plurality of users as user features, and based on the user features, creates a relationship graph showing a social relationship between the plurality of users, sets one or more users among the plurality of users as a target user group, and based on the relationship graph and the user features of the target user group, adds one or more users, among the plurality of users, having a social relationship with a user included in the target user group to the target user group and determines the resulting target user group as an expanded user group.

Abstract: An information processing apparatus acquires factual features of each of a plurality of users as user features, creates a relationship graph showing a social relationship between the plurality of users based on the user features, sets a target user among the users, sets at least one user related to the target user among the plurality of users as a related user based on the relationship graph, and estimates an attribute of the target user based on the user feature of the target user and the user feature of the related user.

Abstract: An information processing apparatus acquires a factual feature of each of a plurality of users as user features, acquires features regarding a plurality of items as item features from a predetermined database, constructs, based on the user features and the item features, a graph including a plurality of user nodes representing the plurality of users, a plurality of item nodes representing the plurality of items, and links indicating mutual relationships between the plurality of user nodes and the plurality of item nodes, and extracts a node representation, in the graph, of any node of the plurality of user nodes and the plurality of item nodes from the graph.

Abstract: An information processing system identifies a type of a relation between a person of interest and a reference person; determines, in accordance with a determination criterion corresponding to the type of the relation between the person of interest and the reference person, a proximity score indicating a proximity between the person of interest and the reference person based on an index indicating a strength of a relationship between the person of interest and the reference person; and estimates an update necessity of personal information on the person of interest based on input data including an attribute of the person of interest, an attribute of the reference person, a change status of personal information on the reference person, and the proximity score and the type of the relation for a pair of the person of interest and the reference person.

Abstract: A real-time, stream data information integration and analytics system can include an information engine that performs real-time entity extraction to create key-value pairs of attributes for a personal profile and integrates similar personal profiles generated from same or different data sources into a single person-of-interest profile. The real-time, stream data information integration and analytics system can further include a real-time analytics module that performs a variety of analytics using the person-of-interest profiles and updates the person-of-interest profiles with scores and other results of the variety of analytics. The variety of real-time analytics can include sentiment analysis and at least some aspects of threat detection analysis. The sentiment analysis can be used to provide a recommender system and the threat detection analysis can be used to identify and predict threats and opportunities.

Abstract: A real-time, stream data information integration and analytics system can include an information engine that performs real-time entity extraction to create key-value pairs of attributes for a personal profile and integrates similar personal profiles generated from same or different data sources into a single person-of-interest profile. The real-time, stream data information integration and analytics system can further include a real-time analytics module that performs a variety of analytics using the person-of-interest profiles and updates the person-of-interest profiles with scores and other results of the variety of analytics. The variety of real-time analytics can include sentiment analysis and at least some aspects of threat detection analysis. The sentiment analysis can be used to provide a recommender system and the threat detection analysis can be used to identify and predict threats and opportunities.

Abstract: Systems and methods for determining one or more attributes and their associated values for a user, by mining the user's social network profiles. The systems and methods determine if the value of an attribute of interest for a user is specified on one or more social networking websites. If so, the systems and methods set the attribute value for the user to the specified attribute value. If not, the systems and methods retrieve, from the social networking websites, attribute values for the attribute of interest for friends of the user to form a group of possible attribute values for the user. The method, selects a value from the group of possible attribute values, and then sets the selected value as the attribute value for the user.

Abstract: Systems and methods for determining one or more attributes and their associated values for a user, by mining the user's social network profiles. The systems and methods determine if the value of an attribute of interest for a user is specified on one or more social networking websites. If so, the systems and methods set the attribute value for the user to the specified attribute value. If not, the systems and methods retrieve, from the social networking websites, attribute values for the attribute of interest for friends of the user to form a group of possible attribute values for the user. The method, selects a value from the group of possible attribute values, and then sets the selected value as the attribute value for the user.