{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:07:47Z","timestamp":1760058467410,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["No. 62001133"],"award-info":[{"award-number":["No. 62001133"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>User interaction behavior is influenced by various intentions, which are often asymmetric. Incorporating intention information into sequential recommendation can significantly improve recommendation performance. However, most existing intention modeling methods rely on auxiliary information or random data augmentation to capture user intentions, which cannot effectively capture the potential correlations between different user intentions, especially when dealing with asymmetric intentions. Furthermore, using random data augmentation methods may amplify the noise in the original sequence, leading to a decline in the model\u2019s recommendation performance. To address these issues, this paper proposes a recommendation model based on Global Intention Learning and Sequence Augmentation. Firstly, a novel sequence information extraction module is designed, which efficiently integrates the refined global item association graph into item representations through a self-supervised approach, thereby capturing global collaborative sequence information. Secondly, an improved sequence augmentation strategy is adopted to reduce the disruption of the original item correlations, making the intention representation more accurate. Finally, intention information is integrated into the sequential recommendation model through a contrastive learning method, further enhancing the accuracy of the model\u2019s recommendations. Experimental results show that compared to several state-of-the-art methods, the proposed model exhibits significant improvements on the Sports, Toys and LastFM datasets.<\/jats:p>","DOI":"10.3390\/sym17040586","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T10:05:51Z","timestamp":1744365951000},"page":"586","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Recommendation Model Based on Global Intention Learning and Sequence Augmentation"],"prefix":"10.3390","volume":"17","author":[{"given":"Minghui","family":"Li","sequence":"first","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Wei","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Xiaodong","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wu, L., Zheng, Z., Qiu, Z., Wang, H., Gu, H., Shen, T., Qin, C., Zhu, C., Zhu, H., and Liu, Q. 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