Abstract: A system and method for recommending items to a user is provided. The system could combine recommendations provided by multiple recommenders by: a) calculating for each recommender j a maximum score Pj for the recommended n items as a function (e.g., sum) of stored recommender ratings for the n items, b) calculating a minimum acceptable score for each recommender system j as a function of the maximum score Pj and a predetermined tradeoff factor ?j such that the minimum acceptable score for at least one recommender system j is less than the maximum score Pj, c) selecting at least one set of items from the plurality of items, such that scores Pj (and/or sum of scores Pj) calculated for the selected set of items for each recommender system j are greater than the respective minimum acceptable score for that recommender system j, and d) identifying selected set of items to the user.

Abstract: Aspects of the present disclosure are directed to systems and methods that facilitate the recommendation of an item to a user. One particular aspect includes a method including the steps of: a) receiving a user request for a new recommendation including rating information for previously-selected items, b) retrieving a mean vector and a covariance matrix representing estimates of prior ratings for a plurality of items by other users, c) calculating a selection likelihood statistics as a function of the estimated mean vector, the estimated covariance matrix and the rating information provided by the, d) calculating ranking statistics as a function of the selection likelihood statistics, and e) transmitting a response to the user recommending at least one of the items not previously selected as a function of the ranking statistics. The recommended item may, for example, be a movie.