KPRLN: deep knowledge preference-aware reinforcement learning network for recommendation

User preference information plays an important role in knowledge graph-based recommender systems, which is reflected in users having different preferences for each entity-relation pair in the knowledge graph. Existing approaches have not modeled this fine-grained user preference feature well, as affecting the performance of recommender systems. In this paper, we propose a deep knowledge preference-aware reinforcement learning network (KPRLN) for the recommendation, which builds paths between user's historical interaction items in the knowledge graph, learns the preference features of each user-entity-relation and generates the weighted knowledge graph with fine-grained preference features. First, we proposed a hierarchical propagation path construction method to address the problems of the pendant entity and long path exploration in the knowledge graph. The method expands outward to form clusters centered on items and uses them to represent the starting and target states in reinforcement learning. With the iteration of clusters, we can better learn the pendant entity preference and explore farther paths. Besides, we design an attention graph convolutional network, which focuses on more influential entity-relation pairs, to aggregate user and item higher order representations that contain fine-grained preference features. Finally, extensive experiments on two real-world datasets demonstrate that our method outperforms other state-of-the-art baselines.