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Inf. Syst."],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>\n            A Session-Based Recommendation (SBR) seeks to predict users\u2019 future item preferences by analyzing their interactions with previously clicked items. In recent approaches, Graph Neural Networks (GNNs) have been commonly applied to capture item relations within a session to infer user intentions. However, these GNN-based methods typically struggle with feature ambiguity between the sequential session information and the item conversion within an item graph, which may impede the model\u2019s ability to accurately infer user intentions. In this article, we propose a novel Multi-hop Multi-view Memory Transformer (M\n            <jats:sup>3<\/jats:sup>\n            T) to effectively integrate the sequence-view information and relation conversion (graph-view information) of items in a session. First, we propose a Multi-view Memory Transformer (M\n            <jats:sup>2<\/jats:sup>\n            T) module to concurrently obtain multi-view information of items. Then, a set of trainable memory matrices are employed to store sharable item features, which mitigates cross-view item feature ambiguity. To comprehensively capture latent user intentions, an M\n            <jats:sup>3<\/jats:sup>\n            T framework is designed to integrate user intentions across different hops of an item graph. Specifically, a k-order power method is proposed to manage the item graph to alleviate the over-smoothing problem when obtaining high-order relations of items. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our method.\n          <\/jats:p>","DOI":"10.1145\/3663760","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T07:13:55Z","timestamp":1715152435000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Multi-Hop Multi-View Memory Transformer for Session-Based Recommendation"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8349-3597","authenticated-orcid":false,"given":"Xingrui","family":"Zhuo","sequence":"first","affiliation":[{"name":"Hefei University of Technology, Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), School of Computer Science and Information Engineering, and Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9488-2208","authenticated-orcid":false,"given":"Shengsheng","family":"Qian","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1277-6802","authenticated-orcid":false,"given":"Jun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computing, National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7072-0262","authenticated-orcid":false,"given":"Fuxin","family":"Dai","sequence":"additional","affiliation":[{"name":"WeChat, Tencent, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6259-392X","authenticated-orcid":false,"given":"Kangyi","family":"Lin","sequence":"additional","affiliation":[{"name":"WeChat, Tencent, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2289-1679","authenticated-orcid":false,"given":"Gongqing","family":"Wu","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), School of Computer Science and Information Engineering, and Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"issue":"6","key":"e_1_3_2_2_2","first-page":"734","article-title":"Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions","volume":"17","author":"Adomavicius Gediminas","year":"2005","unstructured":"Gediminas Adomavicius and Alexander Tuzhilin. 2005. 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