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Inf. Syst."],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:p>A key challenge in personalized product search is to capture user\u2019s preferences. Recent work attempted to model sequences of user historical behaviors, i.e., product purchase histories, to build user profiles and to personalize results accordingly. Although these approaches have demonstrated promising retrieval performances, we notice that most of them focus solely on the intra-sequence interactions between items. However, as there is usually a small amount of historical behavior data, the user profiles learned by these approaches could be very sensitive to the noise included in it. To tackle this problem, we propose incorporating out-of-sequence external information to enhance user modeling. More specifically, we inject the external item\u2013item relations (e.g., belonging to the same brand), and query\u2013query relations (e.g., the semantic similarities between them), into the intra-sequence interaction to learn better user profiles. In addition, we devise two auxiliary decoders, with the historical item sequence reconstruction task and the global item similarity prediction task, to further improve the reliability of user modeling. Experimental results on two datasets from simulated and real user search logs respectively show that the proposed personalized product search method outperforms existing approaches.<\/jats:p>","DOI":"10.1145\/3726864","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T20:17:21Z","timestamp":1743193041000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Sequential Personalized Product Search with External Out-of-sequence Knowledge"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3946-9178","authenticated-orcid":false,"given":"Jiongnan","family":"Liu","sequence":"first","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9781-948X","authenticated-orcid":false,"given":"Zhicheng","family":"Dou","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1556-3335","authenticated-orcid":false,"given":"Jian-Yun","family":"Nie","sequence":"additional","affiliation":[{"name":"DIRO, University de Montreal, Montreal, Quebec, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0838-8348","authenticated-orcid":false,"given":"Zhenlin","family":"Chen","sequence":"additional","affiliation":[{"name":"JD.com Inc, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9586-4652","authenticated-orcid":false,"given":"Guoyu","family":"Tang","sequence":"additional","affiliation":[{"name":"JD.com Inc, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0345-334X","authenticated-orcid":false,"given":"Sulong","family":"Xu","sequence":"additional","affiliation":[{"name":"JD.com Inc, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9777-9676","authenticated-orcid":false,"given":"Ji-Rong","family":"Wen","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China, Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education, Beijing, China, and Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357980"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482276"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080813"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3361738"},{"key":"e_1_3_3_6_2","volume-title":"5th International Conference on Learning Representations (ICLR \u201917)","author":"Ball\u00e9 Johannes","year":"2017","unstructured":"Johannes Ball\u00e9, Valero Laparra, and Eero P. 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