Abstract: Systems, methods, and non-transitory computer readable media are configured to apply a machine learning model to predict an age division for a user based on user information. An age bracket within the age division including a largest number of connections of the user can be determined. The determined age bracket can be assigned for the user.

Abstract: A method to obfuscate the accurate detection of demographic information of a new user who provides ratings to a digital content service having a recommender system includes training an inference engine to detect the 5 demographic information. The training set includes movie ratings and demographic information from a plurality of other users. The new user inputs ratings, such as movie ratings, and the inference engine determines the demographic information of the new user. An obfuscation engine then adds movie ratings to the recommender system which cause an inference engine in the recommender system to fail to accurately detect the demographic information of the new user.

Abstract: A user has an inherent predisposition to have an interest for a particular item. The user's interests may also be affected by what people in her social circle are interested in. To more accurately make recommendations, a user's inherent interests, social influence, how a user responds to recommendations, and/or the user's desire for novelty are taken into consideration. Considering the evolution of users' interests in response to the users' social interactions and users' interactions with the recommender system, the recommendation problem is formulated as an optimization problem to maximize the overall expected utilities of the recommender system. Tractable solutions to the optimization problem are presented for some use cases: (1) when the system does not perform personalization; (2) when the users in the system exhibit attraction dominant behavior; and (3) when the users in the system exhibit aversion dominant behavior.

Abstract: The current methods and apparatus provide a system that learns a private attribute, such as gender, based on at least one iteration of presenting an item to a user and receiving ratings from the user for this item. In an exemplary embodiment, the system may solicit ratings for strategically selected items, such as movies for example, and then infers the user's gender. Based on the assessed confidence in the demographic selected, the system may repeat the selection, presentation and ratings of another item. The proposed system can strategically select the sequence of items that are presented to the user for a rating. By selecting the next item to be rated based on a maximum posterior probability confidence, a demographic with a certain threshold of confidence can be inferred. The inventive arrangements are based on novel usage of Bayesian matrix factorization in an active learning setting.

Abstract: A method and apparatus for protecting user privacy in a recommender system are described including determining what information to release to a user for a movie, transmitting the information to the user, accepting obfuscated input from the user and estimating the user's non-private feature vector. Also described are a method and apparatus for protecting user privacy in a recommender system including receiving movie information, accepting a user's movie feedback, accepting user's private information, calculating an obfuscation value and transmitting the obfuscation value.

Abstract: The present principles consider stochastic bandits with side observations, a model that accounts for both the exploration/exploitation dilemma and relationships between arms. In this setting, after pulling an arm i, the decision maker also observes the rewards for some other actions related to i. The present principles provide a method and a system for efficiently leveraging additional information based on the responses provided by other users connected to the user via a computerized social network and derive new bounds improving on standard regret guarantees. We will see that this model is suited to content recommendation in social networks, where users' reactions may be endorsed or not by their friends.

Abstract: A method for recommending at least one element of an audio-visual program commences by first determining the success of an audio-visual program composed of a set elements initially selected by a user. Thereafter, a replacement element is substituted for at least one of the initially selected elements and the success of the audio-visual program with the replacement element is determined. Thereafter, the replacement element is recommended for substitution in place of the at least one initially selected element if substituting replacement element in the audio-visual program yields greater success.

Abstract: A method is provided for collecting and processing information of a target who is a user of a communication network. The method includes obtaining a first identifier of the target, accessing, based on a handle of the first identifier, a first public webpage associated with the target in a first Internet site identified based on a domain of the first identifier, extracting content of the first public webpage for including in target data of the target, obtaining a third identifier of the target, intercepting a document associated with the target from a private portion of communication network traffic identified based on a domain of the third identifier, extracting content of the document for including in the target data, determining a second identifier by searching the target data, associating the second identifier with the target based on a pre-determined criterion, and collecting information of the target based on the second identifier.

Abstract: A method to determine demographic information of a new user utilizing only ratings includes training an inference engine informed with a training data set which includes ratings and demographic information from a plurality of other users. The new user inputs ratings, such as movie ratings, and an inference engine determines demographic information of the new user. The demographic information of the new user can then be used to provide recommendations or to provide targeted advertisements to the new user.

Abstract: A method for observing social network propagation commences by establishing a graph of the social network, the graph having nodes and edges. Thereafter a graph prior is determined that reflects the graph's structure. A set of edge probabilities between nodes in the graph is iteratively optimized a using the graph prior, wherein each of said edge probabilities represents a probability of a first node influencing a second node.

Abstract: A method performed by a recommender system to recommend items to a new user includes calculating reward estimates from multiple multi-armed bandit models of a user and her social network friends. The new user's social network friends have multi-armed bandit models that are well established. The mixed multi-armed bandit estimates are processed to select the arm that maximizes the estimated reward to the new user. The multi-armed bandit arm of the greatest reward estimate is played and the new user responds by providing feedback so that the new user's multi-armed bandit model is updated as time progresses.

Abstract: A system, apparatus and method for performing social media analytics for a movie are provided. The present disclosure provides for a social media analytics platform that builds a rich landscape of interests of movie audiences by mining data from social networking or microblogging services, such as Twitter™. The present disclosure provides for associating at least one user with a movie; collecting, for the at least one associated user, at least one of user location data, user interest data, user-cited website data, and user television viewing habits data from a social networking or microblogging service; processing the collected data to generate movie campaign data, the movie campaign data including at least one of movie marketing data, movie advertising data, and movie distribution data; and providing the at least one movie marketing data, movie advertising data, and movie distribution data for display in a user interface.

Abstract: The present disclosure is directed to systems, methods, and computer-readable storage media for anonymizing data over multiple temporal releases. Data is received, and nodes and connections in the data are identified. The data also is analyzed to identify predicted connections. The nodes, the connections, and the predicted connections are analyzed to determine how to group the nodes in the data. The data is published, and the grouping of the nodes is extended to subsequent temporal releases of the data, the nodes of which are grouped in accordance with the grouping used with the data.

Abstract: The present disclosure is directed to systems, methods, and computer-readable storage media for anonymizing data over multiple temporal releases. Data is received, and nodes and connections in the data are identified. The data also is analyzed to identify predicted connections. The nodes, the connections, and the predicted connections are analyzed to determine how to group the nodes in the data. The data is published, and the grouping of the nodes is extended to subsequent temporal releases of the data, the nodes of which are grouped in accordance with the grouping used with the data.

Abstract: The present disclosure is directed to systems, methods, and computer-readable storage media for anonymizing data over multiple temporal releases. Data is received, and nodes and connections in the data are identified. The data also is analyzed to identify predicted connections. The nodes, the connections, and the predicted connections are analyzed to determine how to group the nodes in the data. The data is published, and the grouping of the nodes is extended to subsequent temporal releases of the data, the nodes of which are grouped in accordance with the grouping used with the data.

Abstract: The present disclosure is directed to systems, methods, and computer-readable storage media for anonymizing data over multiple temporal releases. Data is received, and nodes and connections in the data are identified. The data also is analyzed to identify predicted connections. The nodes, the connections, and the predicted connections are analyzed to determine how to group the nodes in the data. The data is published, and the grouping of the nodes is extended to subsequent temporal releases of the data, the nodes of which are grouped in accordance with the grouping used with the data.

Abstract: A method of labeling unlabeled nodes in a graph that represents objects that have an explicit structure between them. A computing device can use a labeling engine to labeled nodes in a graph that are labeled and can identify an unlabeled node in the graph that is structurally associated with the labeled nodes. The labeling engine can label the unlabeled node with the label of the labeled node based on the structural association between the unlabeled node and the labeled node.