Advancing Recommender Systems With Deep Reinforcement Learning

Recommender systems (RSs) are crucial in aiding user decisions by providing precise and easily interpretable recommendations. Traditionally, approaches such as contentbased filtering, collaborative filtering, and hybrid methods have been widely adopted in RSs. However, the emergence of Deep Reinforcement Learning (DRL), a branch of machine learning, has demonstrated significant promise in various applications, prompting researchers to explore its potential in RSs. This research applies DRL to improve RS performance by understanding users' preferences and catering to their needs. The study utilizes product information, including features, and historical purchases, to construct a knowledge graph, which forms a basis to use as an environment essential for the Markov Decision Process in the DRL framework. This environment facilitates the generation of a policy that predicts the future products users might be interested in. The findings indicate that DRL-based RSs offer improved performance metrics. By leveraging DRL, RS dynamically adapts to user behavior and preferences, resulting in more personalized recommendations.