Agent Trust Boost via Reinforcement Learning DQN

Trust recommendation system is an important application of recommendation system based on social network. It combines the trust relationship between users to recommend items to users. However, previous studies generally assume that the trust value between users is fixed, so it is unable to respond to the dynamic changes of user trust and preferences in a timely manner, thus affecting the recommendation effect. In fact, after receiving the recommendation, there is a difference between actual evaluation and expected evaluation which is correlated with trust value. The user’s trust in the recommender will increase when the actual evaluation is higher than expected evaluation, and vice versa. Based on the dynamics of trust and the changing process of trust between users, this paper proposes a trust boost method through reinforcement learning. Least mean square algorithm is used to learn the dynamic impact of evaluation difference on user’s trust. In addition, a reinforcement learning method deep q-learning (DQN) is studied to simulate the process of learning user’s preferences and boosting trust value. Finally, a polynomial level algorithm is proposed to calculate the trust value and recommendation, which can motivate the recommender to learn the user’s preference and keep the user’s trust in the recommender at a high level. Experiments indicate that our method applied to recommendation systems could respond to the changes quickly on user’s preferences. Compared with other methods, our method has better accuracy on recommendation.