Deep Reinforcement Learning Recommendation System based on GRU and Attention Mechanism

Recommending personalized content from massive data for users is the key function of the recommendation system. The recommendation process of the traditional recommendation systems is often regarded as static, which cannot reflect the changes of user's real-time interest. This paper addressed this problem and presented a recommendation model that leverages the ability of deep learning methods to effectively deal with decision-making problems. In this model, a state generation module containing gate recurrent unit (GRU) and attention network was designed to obtain user's long and short-term preferences as well as history scores. Then, an actor-critic algorithm was employed to imitate the real-time recommendations. We trained the proposed model and evaluated it on four well-known public datasets. It is proved that the proposed model is superior to existing recommendation models.