{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:36:25Z","timestamp":1760060185904,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T00:00:00Z","timestamp":1754265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Liaoning Provincial Universities","award":["LJ212410152012"],"award-info":[{"award-number":["LJ212410152012"]}]},{"name":"Dalian Polytechnic University","award":["LJ212410152012"],"award-info":[{"award-number":["LJ212410152012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Multi-behavior sequential recommendation (MBSRec) is a form of sequential recommendation. It leverages users\u2019 historical interaction behavior types to better predict their next actions. This approach fits real-world scenarios better than traditional models do. With the rise of the transformer model, attention mechanisms are widely used in recommendation algorithms. However, they suffer from low-pass filtering, and the simple learnable positional encodings in existing models offer limited performance gains. To address these problems, we introduce the context-aware multi-behavior sequential recommendation model (CAMBSRec). It separately encodes items and behavior types, replaces traditional positional encoding with context-similarity positional encoding, and applies the discrete Fourier transform to separate the high and low frequency components and enhance the high frequency components, countering the low-pass filtering effect. Experiments on three public datasets show that CAMBSRec performs better than five baseline models, demonstrating its advantages in terms of recommendation performance.<\/jats:p>","DOI":"10.3390\/informatics12030079","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T10:13:51Z","timestamp":1754475231000},"page":"79","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CAMBSRec: A Context-Aware Multi-Behavior Sequential Recommendation Model"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9781-968X","authenticated-orcid":false,"given":"Bohan","family":"Zhuang","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Lan","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2902-337X","authenticated-orcid":false,"given":"Minghui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.11591\/ijai.v12.i4.pp1803-1811","article-title":"Machine learning based recommender system for e-commerce","volume":"12","author":"Loukili","year":"2023","journal-title":"IAES Int. 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