ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (6): 1227-1238.doi: 10.7544/issn1000-1239.2020.20190403

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Agent Trust Boost via Reinforcement Learning DQN

Qi Faxin1, Tong Xiangrong1, Yu Lei1,2   

  1. 1(School of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005);2(Department of Computer Science, State University of New York at Binghamton, Binghamton, NY 13902)
  • Online:2020-06-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61572418).

Abstract: 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.

Key words: multi-agent systems, reinforcement learning, trust, deep q-learning (DQN), least mean square (LMS)

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