Deep Reinforcement Learning based Recommender System with State Representation

With the scale of E-commerce increasing year by year, the importance of recommender systems is getting increasing attention. Based on deep reinforcement learning, we can model the recommendation task as an interactional and sequential decision procedure between the system and users, instead of a static process. It can improve the recommendation quality to a large extent. By integrating a state representation module, the quality of modeling interaction between users and system can be improved. In this article, we propose a recommender system based on user-commodity state representation integrated deep reinforcement learning, named UCSRDRL, and conduct experiments on the datasets offered by FUXI AI Lab and the outcome performs better than the baseline. The score of UCSRDRL ranked third in the competition.