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.