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: A method, apparatus and system for microphone array calibration include recording audio using at least two microphones, which comprise an array of microphones, using a target microphone of the array of microphones, determining an attenuation factor for audio originating from respective locations of other microphones of the array of microphones, using a target microphone of the array of microphones, determining a delay factor for audio originating from respective locations of other microphones of the array of microphones and implementing the determined attenuation factor and delay factor for removing audio originating from respective locations of the other microphones of the array of microphones from an audio signal captured by the target microphone. The method, apparatus and system then further include removing audio originating from respective locations of the other microphones of the array of microphones from an audio signal captured by the target microphone using beam-forming techniques.

Abstract: Existing recommendation systems leverage user social and demographic information, e.g., age, gender and political affiliation, to personalize content and make recommendations. However, users do not volunteer this information due to privacy concerns or to the lack of initiative in filling out their profile information. The current methods and apparatus provide principles in which the system may learn the private attribute for those users who do not voluntarily disclose them. In an exemplary embodiment, the system receives ratings for items, such as movies, for example, that may be used by a recommendation system. The inventive arrangements are based on novel usage of Bayesian matrix factorization in an active learning setting. Such a system can be carried out using significantly fewer rated items than previously proposed static inference methods. The system functions effectively without sacrificing the quality of the regular recommendations made to the user.

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 of selection that maximizes an expected reward in a contextual multi-armed bandit setting gathers rewards from randomly selected items in a database of items, where the items correspond to arms in a contextual multi-armed bandit setting. Initially, an item is selected at random and is transmitted to a user device which generates a reward. The items and resulting rewards are recorded. Subsequently, a context is generated by the user device which causes a learning and selection engine to calculate an estimate for each arm in the specific context, the estimate calculated using the recorded items and resulting rewards. Using the estimate, an item from the database is selected and transferred to the user device. The selected item is chosen to maximize a probability of a reward from the user device.

Abstract: A method for controlling the average time a peer spends in a swarm of peers in a file sharing system includes first establishing an autonomous mode of operation in the swarm of peers. In the autonomous mode, peers communicate only with other peers in their swarm in order to gain access to pieces of a desired file. If the swarm size meets a threshold size, then the file sharing system switches to a universal mode. In a universal mode, peers from one swarm are permitted to exchange desired file pieces with other peers in other swarms. If the desired file pieces held by peers within a swarm meets a threshold number, then the file sharing system transitions back to the autonomous mode of operation.

Abstract: A method for generating a recommendation item for members of a group includes registering a plurality of users as members of the group, identifying a subgroup of members of the group of users wherein the subgroup requests a recommendation item from a recommendation engine, calculating, using a multi-armed bandit algorithm, a recommendation item for the subgroup of members. The recommendation item is provided to the subgroup members for their evaluation. After evaluating the recommendation item, the individual users rate the recommendation item which updates the recommendation engine with preferences representing the members of the subgroup.

Abstract: Disclosed are methods and apparatus for identifying users of content. The methods include identifying contextual information of a group of users, gathering user access data of the users on the basis of the contextual information of the group of users, analyzing temporal information of the user access data, and identifying particular users in the group of users on the basis of the analyzed temporal information and the contextual information.