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In this manuscript, we develop intention enhanced mixed attentive model () to generate session-based recommendations using two important factors: temporal patterns and estimates of users\u2019 intentions. Unlike existing methods which primarily leverage complicated gated recurrent units to model the temporal patterns,  models the temporal patterns using a light-weight while effective position-sensitive attention mechanism. In , we also leverage the estimate of users\u2019 prospective preferences to signify important items, and generate better recommendations. Our experimental results demonstrate that  models significantly outperform the state-of-the-art methods in six benchmark datasets, with an improvement as much as 19.2%. In addition, our run-time performance comparison demonstrates that during testing,  models are much more efficient than the best baseline method, with a significant average speedup of 47.7 folds.<\/jats:p>","DOI":"10.1007\/s10618-024-01023-0","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T05:01:53Z","timestamp":1717390913000},"page":"2032-2061","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Intention enhanced mixed attentive model for session-based recommendation"],"prefix":"10.1007","volume":"38","author":[{"given":"Bo","family":"Peng","sequence":"first","affiliation":[]},{"given":"Srinivasan","family":"Parthasarathy","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6842-1165","authenticated-orcid":false,"given":"Xia","family":"Ning","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,3]]},"reference":[{"key":"1023_CR1","doi-asserted-by":"crossref","unstructured":"Chen J, Zhu G, Hou H et\u00a0al (2022) Autogsr: Neural architecture search for graph-based session recommendation. 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