Freshness-Aware Thompson Sampling

Abstract. To follow the dynamicity of the user’s content, researchers have recently started to model interactions between users and the Context-Aware Recommender Systems (CARS) as a bandit problem where the system needs to deal with exploration and exploitation dilemma. In this sense, we propose to study the freshness of the user’s content in CARS through the bandit problem. We introduce in this paper an algo-rithm named Freshness-Aware Thompson Sampling (FA-TS) that man-ages the recommendation of fresh document according to the user’s risk of the situation. The intensive evaluation and the detailed analysis of the experimental results reveals several important discoveries in the explo-ration/exploitation (exr/exp) behaviour