Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
This addresses the need for interpretable recommendations in personalized systems, though it builds incrementally on existing knowledge graph approaches.
The paper tackles the problem of generating explainable recommendations by performing explicit reasoning with knowledge graphs, proposing Policy-Guided Path Reasoning (PGPR) which couples recommendation and interpretability through actual paths in a knowledge graph. The method achieves favorable results compared to state-of-the-art methods on several large-scale real-world benchmark datasets.
Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate recommendation, we perform explicit reasoning with knowledge for decision making so that the recommendations are generated and supported by an interpretable causal inference procedure. To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph. Our contributions include four aspects. We first highlight the significance of incorporating knowledge graphs into recommendation to formally define and interpret the reasoning process. Second, we propose a reinforcement learning (RL) approach featuring an innovative soft reward strategy, user-conditional action pruning and a multi-hop scoring function. Third, we design a policy-guided graph search algorithm to efficiently and effectively sample reasoning paths for recommendation. Finally, we extensively evaluate our method on several large-scale real-world benchmark datasets, obtaining favorable results compared with state-of-the-art methods.