Enhancing knowledge graph recommendations through deep reinforcement learning

Recommendation systems are an important tool for information filtering, which have been widely applied in both industry and academia. Although recommendation methods that combine deep learning with collaborative filtering have improved recommendation performance to some extent, issues such as the cold start problem and lack of interpretability remain major challenges. To address these issues, this paper proposes a novel algorithm, RKGnet, a knowledge graph-based recommendation framework using deep reinforcement learning. RKGnet leverages the structural advantages of knowledge graphs and the adaptive decision-making capability of reinforcement learning. By dynamically iterating user preferences within the knowledge graph, RKGnet uncovers the hierarchical latent interests of users and dynamically selects relevant entities through reinforcement learning. It then adapts the recommendation strategy and improves recommendation effectiveness through iterative optimization, enhancing recommendation accuracy and system interpretability. Experimental results demonstrate higher performance when compared with existing methods. RKGnet demonstrates significant advantages in terms of accuracy, robustness, and interpretability, highlighting its broad application prospects in recommendation systems.