A Joint Framework for Explainable Recommendation with Knowledge Reasoning and Graph Representation

With the development of recommendation systems (RSs), researchers are no longer only satisfied with the recommendation results, but also put forward requirements for the recommendation reasons, which helps improve user experience and discover system defects. Recently, some methods develop knowledge graph reasoning via reinforcement learning for explainable recommendation. Different from traditional RSs, these methods generate corresponding paths reasoned from KG to achieve explicit explainability while providing recommended items. But they suffer from a limitation of the fixed representations that are pre-trained on the KG, which leads to a gap between KG representation and explainable recommendation. To tackle this issue, we propose a joint framework for explainable recommendation with knowledge reasoning and graph representation. A sub-graph is constructed from the paths generated through knowledge reasoning and utilized to optimize the KG representations. In this way, knowledge reasoning and graph representation are optimized jointly and form a positive regulation system. Besides, due to more than one candidate in the step of knowledge reasoning, an attention mechanism is also employed to capture the preference. Extensive experiments are conducted on public real-world datasets to show the superior performance of the proposed method. Moreover, the results of the online A/B test on the large-scale Meituan Waimai (MTWM) KG consistently show our method brings benefits to the industry.