Enhancing Product Search with Query-Aware Multi-Facet Explanations through Hierarchical Graph Convolution

Product search is crucial for customers to discover and purchase products. With the growing importance of AI explanations, many KG-based methods use independent reasoning paths to provide explanations for retrieved results. However, these methods often fail to relate explanations to the current query and provide only single-facet explanations rather than multi-facet explanations that address users’ diverse search intents, such as different categories or bands. To overcome this issue, we propose an explainable product search model QGCNM, to generate query-aware multi-facet explanations through hierarchical graph convolution. Specifically, we design a query-aware graph convolutional ranker to excavate user's multi-aspect search intent and develop a multi-path reasoner to explore the intent-based multiple paths for multi-facet explanations. Empirical evaluations on Amazon datasets show that QGCNM outperforms existing models on retrieval effectiveness, and has better explanation abilities.