Optimal modeling of interactions between users and items in recommender systems using an improved deep reinforcement learning method

Recommender systems are one of the most important topics in academia and industry. With the increase in the volume of information and data, it has become confusing and sometimes impossible for users to access the required services without using recommender systems. So far, various techniques have been proposed for this purpose such as collaborative filtering, matrix factorization, logistic regression, neural networks, etc. However, most of these methods suffer from two limitations: (1) considering the recommendation as a static procedure and ignoring the dynamic interactive nature between users and the recommender systems; (2) focusing on the immediate feedback of recommended items and neglecting the long-term rewards. In this research, the modeling of interactions between users and items is done using an improved deep reinforcement learning method which can consider both the dynamic adaptation and long term rewards. The results of the experiments show that the proposed algorithm performs better than other methods.