Recommender systems enhancement using deep reinforcement learning

Recommendation systems provide users with personalized recommendations, stand out as systems aiming to provide appropriate and efficient services. Traditional recommendation systems provide suggestions to their users with static approaches and do not include user preferences that change over time in suggestion strategies. In this study, a comprehensive review and comparison of recommendation systems that can adaptively develop suggestion approaches according to changing user preferences and learn user preferences is presented. In this research other approaches and solutions with frameworks of Deep Reinforcement Learning will be compared. In all articles that is reviewed Markov Decision Process (MDP) were used as a solution for dynamic recommendations and long-term rewards for the users. Proposed frameworks like DEERS, DRR etc. will be reviewed.