ENHANCING RECOMMENDER SYSTEMS WITH DEEP REINFORCEMENT LEARNING AND KNOWLEDGE GRAPH EMBEDDINGS

Abstract

Deep Reinforcement Learning (DRL), a subfield of machine learning, has shown remarkable potential in various domains, including recommender systems (RSs). This study leverages DRL to improve RS performance by effectively modeling user preferences and addressing their unique needs. A knowledge graph (KG) is constructed using product information, such as features and historical purchase data, to serve as the environment for the Markov Decision Process (MDP) within the DRL framework. The KG is enriched with embeddings to enable efficient navigation and enhance its utility. The Actor-Critic model in DRL employs these embeddings within the MDP, enabling a more accurate representation of user preferences. Central to this approach is the Representation of User Preferences via Path Embedding Propagation (RUPPEP), which serves as the study’s core contribution. Experimental results demonstrate that DRL-based RSs achieve superior performance metrics, with a 13.26% improvement in NDCG for the Amazon Cell Phones dataset and a 15.43% increase for the Amazon Beauty dataset compared to the best SOTA baseline model, highlighting their potential to advance the field of recommendation systems.