Query-Aware Explainable Product Search With Reinforcement Knowledge Graph Reasoning

Product search is one of the most effective tools for people to browse and purchase products on e-commerce platforms. Recent advances have mainly focused on ranking products by their likelihood to be purchased through retrieval models. However, they overlook the problem that users may not understand why certain products are retrieved for them. The lack of appropriate explanations can lead to an unsatisfactory user experience and further decrease user trust in the platforms. To address this problem, we propose a Query-aware Explainable Product Search with Reinforcement Knowledge Reasoning, namely QEPS, which uses search behaviors related to the current query to reinforce explanations. Specifically, with the aim of retrieving suitable products with explanations, QEPS takes full advantage of the user-product knowledge graph (KG) and develops a reinforcement learning approach, characterized by the demonstration-guided policy network and query-aware rewards, to perform explicit multi-step reasoning on the KG. The reasoning paths between users and products are automatically derived from the current query-related search behavior, which can provide valuable signals as to why the retrieved products are more likely to satisfy the user's search intent. Empirical experiments on four datasets show that our model achieves remarkable performance and is able to generate reasonable explanations for the search results.