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Inf. Syst."],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>\n            In this work, we present\n            <jats:sc>TriMLP<\/jats:sc>\n            as a foundational MLP-like architecture for the sequential recommendation, simultaneously achieving computational efficiency and promising performance. First, we empirically study the incompatibility between existing purely MLP-based models and sequential recommendation, that the inherent fully-connective structure endows historical user\u2013item interactions (referred as tokens) with unrestricted communications and overlooks the essential chronological order in sequences. Then, we propose the MLP-based\n            <jats:italic>Triangular Mixer<\/jats:italic>\n            to establish ordered contact among tokens and excavate the primary sequential modeling capability under the standard auto-regressive training fashion. It contains (1) a global mixing layer that drops the lower-triangle neurons in MLP to block the anti-chronological connections from future tokens and (2) a local mixing layer that further disables specific upper-triangle neurons to split the sequence as multiple independent sessions. The mixer serially alternates these two layers to support fine-grained preferences modeling, where the global one focuses on the long-range dependency in the whole sequence, and the local one calls for the short-term patterns in sessions. Experimental results on 12 datasets of different scales from 4 benchmarks elucidate that\n            <jats:sc>TriMLP<\/jats:sc>\n            consistently attains favorable accuracy\/efficiency tradeoff over all validated datasets, where the average performance boost against several state-of-the-art baselines achieves up to 14.88%, and the maximum reduction of inference time reaches 23.73%. The intriguing properties render\n            <jats:sc>TriMLP<\/jats:sc>\n            a strong contender to the well-established RNN-, CNN-, and Transformer-based sequential recommenders. Code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/jiangyiheng1\/TriMLP\">https:\/\/github.com\/jiangyiheng1\/TriMLP<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3670995","type":"journal-article","created":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T12:44:55Z","timestamp":1718023495000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["<scp>TriMLP<\/scp>\n            : A Foundational MLP-Like Architecture for Sequential Recommendation"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6737-0389","authenticated-orcid":false,"given":"Yiheng","family":"Jiang","sequence":"first","affiliation":[{"name":"Lab of Mobile Intelligent Computing (MIC), College of Computer Science and Technology, Jilin University, Changchun, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8370-5011","authenticated-orcid":false,"given":"Yuanbo","family":"Xu","sequence":"additional","affiliation":[{"name":"Lab of Mobile Intelligent Computing (MIC), College of Computer Science and Technology, Jilin University, Changchun, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0056-3626","authenticated-orcid":false,"given":"Yongjian","family":"Yang","sequence":"additional","affiliation":[{"name":"Lab of Mobile Intelligent Computing (MIC), College of Computer Science and Technology, Jilin University, Changchun, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5286-9491","authenticated-orcid":false,"given":"Funing","family":"Yang","sequence":"additional","affiliation":[{"name":"Lab of Mobile Intelligent Computing (MIC), College of Computer Science and Technology, Jilin University, Changchun, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3961-5523","authenticated-orcid":false,"given":"Pengyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, The State Key Laboratory of Internet of Things for Smart City, University of Macau, Zhuhai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9867-1712","authenticated-orcid":false,"given":"Chaozhuo","family":"Li","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9170-7009","authenticated-orcid":false,"given":"Fuzhen","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6016-6465","authenticated-orcid":false,"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Thrust of Artificial Intelligence, Hong Kong University of Science and Technology (Guangzhou), Hong Kong, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Lei Jimmy Ba Jamie Ryan Kiros and Geoffrey E. 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