Social Attentive Deep Q-network for Recommendation

Yu Lei, Zhitao Wang, Wenjie Li, Hongbin Pei

Abstract: While deep reinforcement learning has been successfully applied to recommender systems, it is challenging and unexplored to improve the performance of deep reinforcement learning recommenders by effectively utilizing the pervasive social networks. In this work, we develop a Social Attentive Deep Q-network (SADQN) agent, which is able to provide high-quality recommendations during user-agent interactions by leveraging social influence among users. Specifically, SADQN is able to estimate action-values not only based on the users' personal preferences, but also based on their social neighbors' preferences by employing a particular social attention layer. The experimental results on three real-world datasets demonstrate that SADQN significantly improves the performance of deep reinforcement learning agents that overlook social influence.