Work

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

Year: 2026

Type: article

Abstract: 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.

Source: ACM Transactions on Knowledge Discovery from Data

DOI: 10.1145/3802580

Authors: Qiannan Zhu, Qing He, Lingzhi Wang, Mingming Li

Institutions: Chinese Academy of Sciences, Beijing Normal University, Institute of Information Engineering, University of Pennsylvania

Language: English

Topic: Information Retrieval and Search Behavior

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities

Awards: G3861646076, G468414969