A Bayesian User-Controllable Recommender System

ABSTRACT In this research, we propose a Bayesian User-Controllable Recommender System. Our approach allows the user to control the contextual information, i.e., the user can define the content (other users and items) and parameters (users, items, novelty and popularity) used by the recommender to compute predictions. To demonstrate the usefulness of our proposal, we present different scenarios where we change the context configuration and discuss the system outputs. MOTIVATION AND RESEARCH CHAL-LENGES Historically, the main goal of the Recommender Systems (RS) have been increasing the accuracy of the recommendation dation, e.g., climate weather information, demographic informatio