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Intell. Syst. Technol."],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>\n            In recent years,\n            <jats:bold>session-based recommender systems (SRSs)<\/jats:bold>\n            have emerged as a significant research focus within the recommendation field. Capturing user intentions to infer user interest accordingly has proven to be effective in enhancing the accuracy of SRSs. However, existing techniques assume that all sessions have the same number of intentions or that the items in one category belonging to the same session reflect the same intention. In real applications, such as e-commerce, sessions may have different numbers of intentions, and the same type of items in a session may correspond to different intentions. As a result, existing techniques cannot guarantee high-quality user interest prediction. In this article, we propose a novel\n            <jats:bold>Adaptive Intention Learning Network (AILN)<\/jats:bold>\n            to capture an adaptive number of intentions for each session, thereby enhancing the accuracy of user interest inference. Specifically, we design an\n            <jats:bold>intention evaluation network (IEN)<\/jats:bold>\n            to evaluate whether a subsequence of a session corresponds to a valid intention, and an\n            <jats:bold>intention generation network (IGN)<\/jats:bold>\n            to learn the representation of a valid intention. By checking each subsequence of a session, IEN and IGN enable the incremental learning of a session-specific\n            <jats:bold>intention hierarchy (IH)<\/jats:bold>\n            to store valid intentions of the session. To reduce the cost of building the IH, we propose a pruning strategy that exploits the intention validity to avoid unnecessary evaluation. The representative intentions are selected from IH and input into a designed\n            <jats:italic>interest predictor<\/jats:italic>\n            to infer the user interest. Experimental results on two real-world datasets demonstrate the superiority of our proposed AILN.\n          <\/jats:p>","DOI":"10.1145\/3709004","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T14:59:26Z","timestamp":1734965966000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Adaptive Intention Learning for Session-Based Recommendation"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7832-7148","authenticated-orcid":false,"given":"Qingbo","family":"Zhang","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Northeastern University, Shenyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6184-4771","authenticated-orcid":false,"given":"Xiaochun","family":"Yang","sequence":"additional","affiliation":[{"name":"Northeastern University, Shenyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5254-3491","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"Northeastern University, Shenyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2694-1023","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, China, and Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Ministry of Education, Shenyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3350-7022","authenticated-orcid":false,"given":"Zhu","family":"Sun","sequence":"additional","affiliation":[{"name":"Singapore University of Technology and Design, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1302-818X","authenticated-orcid":false,"given":"Xiangmin","family":"Zhou","sequence":"additional","affiliation":[{"name":"RMIT University, Melbourne, Australia"}]}],"member":"320","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2792838.2799684"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775109"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339643"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411897"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3640457.3688118"},{"key":"e_1_3_3_8_2","unstructured":"Jun Fang. 2021. 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