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Recomm. Syst."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n            Nowadays, research on session-based recommender systems (SRSs) is one of the hot spots in the recommendation domain. Existing methods make recommendations based on the user\u2019s current intention (also called short-term preference) during a session, often overlooking the specific preferences associated with these intentions. In reality, users usually exhibit diverse preferences for different intentions, and even for the same intention, individual preferences can vary significantly between users. As users interact with items throughout a session, their intentions can shift accordingly. To enhance recommendation quality, it is crucial not only to consider the user\u2019s intentions but also to dynamically learn their varying preferences as these intentions change. In this article, we propose a novel\n            <jats:bold>I<\/jats:bold>\n            ntention-sensitive\n            <jats:bold>P<\/jats:bold>\n            reference\n            <jats:bold>L<\/jats:bold>\n            earning\n            <jats:bold>N<\/jats:bold>\n            etwork (IPLN) including three main modules:\n            <jats:italic toggle=\"yes\">intention recognizer<\/jats:italic>\n            ,\n            <jats:italic toggle=\"yes\">preference detector<\/jats:italic>\n            , and\n            <jats:italic toggle=\"yes\">prediction layer<\/jats:italic>\n            . Specifically, the\n            <jats:italic toggle=\"yes\">intention recognizer<\/jats:italic>\n            infers the user\u2019s underlying intention within his\/her current session by analyzing complex relationships among items. Based on the acquired intention, the\n            <jats:italic toggle=\"yes\">preference detector<\/jats:italic>\n            learns the intention-specific preference by selectively integrating latent features from items in the user\u2019s historical sessions. Besides, the user\u2019s general preference is utilized to refine the obtained preference to reduce the potential noise carried from historical records. Ultimately, the fine-tuned preference and intention collaborate to instruct the next-item recommendation in the\n            <jats:italic toggle=\"yes\">prediction layer<\/jats:italic>\n            . To prove the effectiveness of the proposed IPLN, we perform extensive experiments on two real-world datasets. The experiment results demonstrate the superiority of IPLN compared with other state-of-the-art models.\n          <\/jats:p>","DOI":"10.1145\/3727647","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T07:25:03Z","timestamp":1743405903000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Intention-sensitive Preference Learning Network for Personalized Session-based Recommendation"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7832-7148","authenticated-orcid":false,"given":"Qingbo","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University","place":["Shenyang, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6184-4771","authenticated-orcid":false,"given":"Xiaochun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University","place":["Shenyang, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5254-3491","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University","place":["Shenyang, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2694-1023","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University","place":["Shenyang, China"]},{"name":"National Frontiers Science Center for Industrial Intelligence and Systems Optimization","place":["Shenyang, China"]},{"name":"Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Ministry of Education","place":["Shenyang, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1302-818X","authenticated-orcid":false,"given":"Xiangmin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computing Technologies, RMIT University","place":["Melbourne, Australia"]}]}],"member":"320","published-online":{"date-parts":[[2025,7,29]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"241","volume-title":"Proceedings of the 9th ACM Conference on Recommender Systems (RecSys\u201915)","author":"Aghdam Mehdi Hosseinzadeh","year":"2015","unstructured":"Mehdi Hosseinzadeh Aghdam, Negar Hariri, Bamshad Mobasher, and Robin D. 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