{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:59:21Z","timestamp":1781153961461,"version":"3.54.1"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71572125"],"award-info":[{"award-number":["71572125"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Sequential recommendation systems aim to capture both short-term and long-term user preferences by modeling temporal patterns in user behavior. Recently, frequency-domain models have gained attention for their ability to capture global patterns efficiently. However, most existing methods rely on Fourier transforms, which are prone to Gibbs phenomenon when processing non-periodic signals, introducing high-frequency noise, and incurring substantial computational overhead due to complex-number operations. To overcome these issues, we propose Discrete Cosine transform-enhanced Hybrid Frequency-Attention Network for Sequential Recommendation (DCAN-Rec), a hybrid frequency-attention model that combines frequency information with self-attention to improve recommendation accuracy and reduce computation costs. Instead of Fourier transforms, DCAN-Rec uses discrete cosine transforms to optimize frequency-domain representations, reduce computational overhead, and mitigate high-frequency noise interference. It employs a learnable filter-based multilayer perceptron to separately extract features from high- and low-frequency components of user behavior. Experiments on seven real-world datasets show that DCAN-Rec effectively captures both global trends and local patterns in user sequences, consistently outperforming state-of-the-art methods. On average, DCAN-Rec improves performance by 16.29% over SASRec (Self-Attentive Sequential Recommendation), and also surpasses FMLPRec (Filter-Enhanced MLP for Sequential Recommendation) by 8.96%, FEARec (Frequency-Enhanced Hybrid Attention Network for Sequential Recommendation) by 3.99%, and DuoRec (Contrastive Learning-Based Model for Alleviating Representation Degeneration in Sequential Recommendation) by 3.75%, demonstrating strong generalization and stability.<\/jats:p>","DOI":"10.1093\/comjnl\/bxaf141","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T12:53:36Z","timestamp":1767617616000},"page":"718-735","source":"Crossref","is-referenced-by-count":1,"title":["Research on hybrid attention sequential recommendation model based on discrete cosine transform frequency enhancement"],"prefix":"10.1093","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0273-0595","authenticated-orcid":false,"given":"Yancong","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information Engineering, Tianjin University of Commerce , 409 Guangrong Road, Beichen District, Tianjin 300134 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8467-8603","authenticated-orcid":false,"given":"Kaiyue","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Tianjin University of Commerce , 409 Guangrong Road, Beichen District, Tianjin 300134 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0049-552X","authenticated-orcid":false,"given":"Weiwei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Management, Zhejiang University of Technology , 288 Liuhe Road, Xihu District, Hangzhou, Zhejiang 310014 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiman","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Tianjin University of Commerce , 409 Guangrong Road, Beichen District, Tianjin 300134 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"2026061100295055700_ref1","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1109\/ICDM.2018.00035","volume-title":"2018 IEEE International Conference on Data Mining (ICDM)","author":"Kang","year":"2018"},{"key":"2026061100295055700_ref2","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1145\/3159652.3159656","volume-title":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","author":"Tang","year":"2018"},{"key":"2026061100295055700_ref3","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.1145\/3485447.3512111","volume-title":"Proceedings of the ACM Web Conference 2022","author":"Zhou","year":"2022"},{"key":"2026061100295055700_ref4","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/3539618.3591689","volume-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Du","year":"2023"},{"key":"2026061100295055700_ref5","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1145\/3488560.3498433","volume-title":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (WSDM 2022)","author":"Qiu","year":"2022"},{"key":"2026061100295055700_ref6","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MIC.2003.1167344","article-title":"Amazon. com recommendations: item-to-item collaborative filtering","volume":"7","author":"Linden","year":"2003","journal-title":"IEEE Internet Comput"},{"key":"2026061100295055700_ref7","volume-title":"Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues","author":"Br\u00e9maud","year":"2013"},{"key":"2026061100295055700_ref8","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1109\/ICDM.2010.127","volume-title":"2010 IEEE International Conference on Data Mining","author":"Rendle","year":"2010"},{"key":"2026061100295055700_ref9","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1145\/2766462.2767694","volume-title":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Wang","year":"2015"},{"key":"2026061100295055700_ref48","author":"Hidasi B"},{"key":"2026061100295055700_ref11","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1109\/TKDE.2018.2881260","article-title":"MV-RNN: a multi-view recurrent neural network for sequential recommendation","volume":"32","author":"Cui","year":"2018","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2026061100295055700_ref49","first-page":"123","article-title":"A long-short demands-aware model for next-item recommendation","volume-title":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, July 21\u201325, Paris, France","author":"Bai","year":"2019"},{"key":"2026061100295055700_ref13","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1145\/3289600.3290975","volume-title":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","author":"Yuan","year":"2019"},{"key":"2026061100295055700_ref14","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1145\/3366423.3380116","volume-title":"Proceedings of The Web Conference 2020","author":"Yuan","year":"2020"},{"key":"2026061100295055700_ref15","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1145\/3357384.3357925","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Song","year":"2019"},{"key":"2026061100295055700_ref16","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-319-46466-4_5","volume-title":"European Conference on Computer Vision (ECCV)","author":"Noroozi","year":"2016"},{"key":"2026061100295055700_ref17","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI blog"},{"key":"2026061100295055700_ref18","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1109\/ICDE53745.2022.00099","volume-title":"2022 IEEE 38th International Conference on Data Engineering (ICDE)","author":"Xie","year":"2022"},{"key":"2026061100295055700_ref19","first-page":"13069","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Ye","year":"2025"},{"key":"2026061100295055700_ref20","first-page":"12183","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Liu","year":"2025"},{"key":"2026061100295055700_ref21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3705727","article-title":"One model for all: large language models are domain-agnostic recommendation systems","volume":"43","author":"Tang","year":"2025","journal-title":"ACM Trans Inf Syst"},{"key":"2026061100295055700_ref50","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1145\/3701551.3703573","article-title":"Reindex-then-adapt: Improving large language models for conversational recommendation","volume-title":"Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, Hannover, Germany","author":"He","year":"2025"},{"key":"2026061100295055700_ref23","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1109\/MSP.2020.3014594","article-title":"Graph signal processing and deep learning: convolution, pooling, and topology","volume":"37","author":"Cheung","year":"2020","journal-title":"IEEE Signal Process Mag"},{"key":"2026061100295055700_ref24","first-page":"980","article-title":"Global filter networks for image classification","volume":"34","author":"Rao","year":"2021","journal-title":"Adv Neural Inf Process Syst"},{"key":"2026061100295055700_ref25","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1145\/3604915.3608790","volume-title":"Proceedings of the 17th ACM Conference on Recommender Systems","author":"Zhang","year":"2023"},{"key":"2026061100295055700_ref26","doi-asserted-by":"publisher","first-page":"123118","DOI":"10.1016\/j.eswa.2023.123118","article-title":"TFCSRec: time\u2013frequency consistency based contrastive learning for sequential recommendation","volume":"245","author":"Xiao","year":"2024","journal-title":"Expert Syst Appl"},{"key":"2026061100295055700_ref51","first-page":"8984","article-title":"An attentive inductive bias for sequential recommendation beyond the self-attention","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence, February 20\u201327, Vancouver, Canada","author":"Shin","year":"2024"},{"key":"2026061100295055700_ref28","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1109\/CSCWD61410.2024.10580748","volume-title":"2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","author":"Fan","year":"2024"},{"key":"2026061100295055700_ref29","first-page":"435","volume-title":"The 41st International ACM Sigir Conference on Research & Development in Information Retrieval","author":"Lu","year":"2018"},{"key":"2026061100295055700_ref30","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1145\/2396761.2396780","volume-title":"Proceedings of the 21st ACM International Conference on Information and Knowledge Management","author":"Hofmann","year":"2012"},{"key":"2026061100295055700_ref31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3569453","article-title":"Doubly robust estimation for correcting position bias in click feedback for unbiased learning to rank","volume":"41","author":"Oosterhuis","year":"2023","journal-title":"ACM Trans Inf Syst"},{"key":"2026061100295055700_ref32","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1145\/2959100.2959141","volume-title":"Proceedings of the 10th ACM Conference on Recommender Systems","author":"Liu","year":"2016"},{"key":"2026061100295055700_ref33","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1145\/3404835.3462875","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and development in Information Retrieval","author":"Zhang","year":"2021"},{"key":"2026061100295055700_ref34","first-page":"1670","volume-title":"International Conference on Machine Learning","author":"Schnabel","year":"2016"},{"key":"2026061100295055700_ref52","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1145\/3437963.3441800","article-title":"Denoising implicit feedback for recommendation","volume-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining, March 8\u201312, Virtual Event (Israel)","author":"Wang","year":"2021"},{"key":"2026061100295055700_ref53","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.24963\/ijcai.2021\/218","article-title":"Does every data instance matter? Enhancing sequential recommendation by eliminating unreliable data","volume-title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, August 19\u201327, Montreal, Canada","author":"Sun","year":"2021"},{"key":"2026061100295055700_ref54","first-page":"3297","article-title":"END4Rec: Efficient noise-decoupling for multi-behavior sequential recommendation","volume-title":"Proceedings of The Web Conference, May 13\u201317, Singapore, Singapore","author":"Han","year":"2024"},{"key":"2026061100295055700_ref38","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1145\/3477495.3532059","volume-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Gao","year":"2022"},{"key":"2026061100295055700_ref39","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/BF01208562","article-title":"The Gibbs phenomenon for best L 1-trigonometric polynomial approximation","volume":"11","author":"Moskona","year":"1995","journal-title":"Construct Approx"},{"key":"2026061100295055700_ref40","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/S0377-0427(03)00500-4","article-title":"Towards the resolution of the Gibbs phenomena","volume":"161","author":"Shizgal","year":"2003","journal-title":"J Comput Appl Math"},{"key":"2026061100295055700_ref41","doi-asserted-by":"publisher","first-page":"102158","DOI":"10.1016\/j.aei.2023.102158","article-title":"FECAM: frequency enhanced channel attention mechanism for time series forecasting","volume":"58","author":"Jiang","year":"2023","journal-title":"Adv Eng Inf"},{"key":"2026061100295055700_ref55","doi-asserted-by":"crossref","DOI":"10.1137\/1.9780898719512","article-title":"Matrix analysis and applied linear algebra","volume-title":"Society for Industrial and Applied Mathematics (SIAM)","author":"Meyer","year":"2000"},{"key":"2026061100295055700_ref56","article-title":"Continuous and discrete signals and systems","author":"Soliman","year":"1990"},{"key":"2026061100295055700_ref44","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1016\/j.elerap.2010.04.006","article-title":"The influence of the commercial features of the internet on the adoption of e-commerce by consumers","volume":"9","author":"Crespo","year":"2010","journal-title":"Electron Commer Res Appl"},{"key":"2026061100295055700_ref57","article-title":"Understanding and improving layer normalization","volume-title":"Advances in Neural Information Processing Systems, Vancouver, BC, Canada, December 8\u201314, 2019","author":"Xu","year":"2019"},{"key":"2026061100295055700_ref46","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1145\/3357384.3357895","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Sun","year":"2019"},{"key":"2026061100295055700_ref47","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/ICDM.2016.0030","volume-title":"2016 IEEE 16th international conference on data mining (ICDM)","author":"He","year":"2016"}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/69\/4\/718\/66261027\/bxaf141.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/69\/4\/718\/66261027\/bxaf141.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:30:10Z","timestamp":1781152210000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/69\/4\/718\/8413847"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,5]]},"references-count":47,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,1,5]]},"published-print":{"date-parts":[[2026,4,18]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxaf141","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"value":"0010-4620","type":"print"},{"value":"1460-2067","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,4]]},"published":{"date-parts":[[2026,1,5]]}}}