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Knowl. Discov. Data"],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>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\u2019 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\u2019s 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.<\/jats:p>","DOI":"10.1145\/3802580","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T14:22:54Z","timestamp":1774621374000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Product Search with Query-Aware Multi-Facet Explanations through Hierarchical Graph Convolution"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9502-478X","authenticated-orcid":false,"given":"Qiannan","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing, China and Beijing Key Laboratory of Artificial Intelligence for Education, Beijing, China and Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education, Beijing, China, Stuart Weitzman School of Design, University of Pennsylvania, Philadelphia, Pennsylvania, USA, School of Artificial Intelligence, Beijing Normal University, Beijing, China, and Chinese Academy of Sciences Institute of Information Engineering, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1998-5772","authenticated-orcid":false,"given":"Qing","family":"He","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing, China and Beijing Key Laboratory of Artificial Intelligence for Education, Beijing, China and Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education, Beijing, China, Stuart Weitzman School of Design, University of Pennsylvania, Philadelphia, Pennsylvania, USA, School of Artificial Intelligence, Beijing Normal University, Beijing, China, and Chinese Academy of Sciences Institute of Information Engineering, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1703-8336","authenticated-orcid":false,"given":"Lingzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing, China and Beijing Key Laboratory of Artificial Intelligence for Education, Beijing, China and Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education, Beijing, China, Stuart Weitzman School of Design, University of Pennsylvania, Philadelphia, Pennsylvania, USA, School of Artificial Intelligence, Beijing Normal University, Beijing, China, and Chinese Academy of Sciences Institute of Information Engineering, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5811-0681","authenticated-orcid":false,"given":"Mingming","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing, China and Beijing Key Laboratory of Artificial Intelligence for Education, Beijing, China and Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education, Beijing, China, Stuart Weitzman School of Design, University of Pennsylvania, Philadelphia, Pennsylvania, USA, School of Artificial Intelligence, Beijing Normal University, Beijing, China, and Chinese Academy of Sciences Institute of Information Engineering, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/a11090137"},{"key":"e_1_3_2_3_2","first-page":"379","volume-title":"Proceedings of CIKM2019","author":"Qingyao Ai","year":"2019","unstructured":"Qingyao Ai, Daniel N. 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Tetreault, and Alex Jaimes. 2024. Dissecting users\u2019 needs for search result explanations. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI \u201924). ACM, Honolulu, HI, Article 841, 1\u201317."},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511964"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411936"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531840"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783381"},{"key":"e_1_3_2_24_2","unstructured":"Navid Mehrdad Hrushikesh Mohapatra Mossaab Bagdouri Prijith Chandran Alessandro Magnani Xunfan Cai Ajit Puthenputhussery Sachin Yadav Tony Lee ChengXiang Zhai et al. 2024. Large language models for relevance judgment in product search. arXiv:2406.00247. 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Explainable recommendation: Theory and applications. arXiv: 1708.06409. 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