罗格斯大学发表的一篇论文《Reinforcement Knowledge Graph Reasoning forExplainable Recommendation》已被SIGIR 2019接收。文中提出了一种称为策略引导路径推理（PGPR）的方法，该方法通过在知识图中提供实际路径来结合推荐和可解释性。该论文实验结果表示，PGPR方法在NDCG, Hit Rate, Recall 和Precision方面始终优于所有数据集上的所有其他baselines。
Reinforcement Knowledge Graph Reasoning forExplainable Recommendation
Yikun Xian、Zuohui Fu、S. Muthukrishnan、Gerard de Melo、Yongfeng Zhang
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.