{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T10:59:26Z","timestamp":1773572366835,"version":"3.50.1"},"reference-count":67,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2024A1515010122"],"award-info":[{"award-number":["2024A1515010122"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62461160311, 62272315 and 62172283"],"award-info":[{"award-number":["62461160311, 62272315 and 62172283"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Recomm. Syst."],"published-print":{"date-parts":[[2026,9,30]]},"abstract":"<jats:p>\n                    Multi-behavior sequential recommendation (MBSR), which captures sequential patterns and behavioral heterogeneity to model users\u2019 multifaceted preferences, has shown promising results. Despite their effectiveness, existing methods often suffer from performance degradation due to inherent data sparsity in real-world scenarios. Current data augmentation methods in recommendation systems predominantly focus on single-behavior modeling, failing to account for the diversity of user preference expressions across different types of behaviors. Moreover, conventional augmentation strategies risk introducing noise or irrelevant patterns during sample generation, potentially distorting the next-item prediction task. To address these challenges, we propose a novel and generic framework called multi-behavior data augmentation for sequential recommendation (MBASR). Specifically, we propose five distinct behavior-aware data augmentation operations, which are designed based on interactions both within and across subsequences, to generate diverse and enriched training samples. Each augmentation operation leverages correlations between behaviors or similarities among users, ensuring that the enhanced data remains aligned with users\u2019 natural behavior patterns. Furthermore, we introduce a combined augmentation method, merging two data augmentation operations to achieve better results. In addition, we introduce two position-based sampling strategy that can effectively reduce the perturbation brought by the augmentation operations to the original data. Notably, as a data-centric solution, our MBASR can be seamlessly integrated into various MBSR models without modifying their underlying structures. Comprehensive evaluations on four real-world datasets validate the efficacy of our MBASR, demonstrating significant performance improvement across mainstream MBSR models. The source code, scripts and datasets are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/XiaoQi-C\/MBASR\">https:\/\/github.com\/XiaoQi-C\/MBASR<\/jats:ext-link>\n                  <\/jats:p>","DOI":"10.1145\/3749998","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T11:14:19Z","timestamp":1753269259000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["MBASR: A Generic Framework for Multi-Behavior Data Augmentation in Sequential Recommendation"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5563-8000","authenticated-orcid":false,"given":"Qi","family":"Xiao","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8781-6618","authenticated-orcid":false,"given":"Jing","family":"Xiao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6326-9531","authenticated-orcid":false,"given":"Weike","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6933-5760","authenticated-orcid":false,"given":"Zhong","family":"Ming","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University","place":["Shenzhen, China"]},{"name":"Shenzhen Technology University","place":["Shenzhen, China"]},{"name":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557262"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-023-01840-7"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28669"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671755"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-025-02371-z"},{"key":"e_1_3_2_7_2","first-page":"1571","volume-title":"Proceedings of the 37th International Conference on Machine Learning (ICML\u201920)","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (ICML\u201920). 1571\u20131607."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3523227.3546761"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25537"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109877"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591994"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3568022"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3497510"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.06.084"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0030"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271761"},{"key":"e_1_3_2_18_2","volume-title":"Proceedings of the 4th International Conference on Learning Representations (ICLR\u201916)","author":"Hidasi Bal\u00e1zs","year":"2016","unstructured":"Bal\u00e1zs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In Proceedings of the 4th International Conference on Learning Representations (ICLR\u201916)."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_3_2_20_2","doi-asserted-by":"crossref","unstructured":"Juyong Jiang Peiyan Zhang Yingtao Luo Chaozhuo Li Jae Boum Kim Kai Zhang Senzhang Wang Sunghun Kim and Philip S. Yu. 2021. Improving sequential recommendations via bidirectional temporal data augmentation with pre-training. IEEE Transactions on Knowledge and Data Engineering 37 5 (2021) 2652\u20132664.","DOI":"10.1109\/TKDE.2025.3546035"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614973"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3640457.3688159"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3640457.3688103"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3616855.3635857"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220014"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412247"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2661760"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3657302"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657716"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5931"},{"key":"e_1_3_2_32_2","unstructured":"Zhiwei Liu Yongjun Chen Jia Li Philip S. Yu Julian McAuley and Caiming Xiong. 2021. Contrastive self-supervised sequential recommendation with robust augmentation. arXiv:2108.06479. Retrieved from https:\/\/arxiv.org\/abs\/2108.06479 (2021)."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463036"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5945"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401098"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3523227.3546785"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498433"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358010"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12143048"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109896"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772773"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611723"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159656"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512147"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380077"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462855"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3616855.3635853"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119911"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671901"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657682"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/icde53745.2022.00099"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.5555\/3367471.3367589"},{"key":"e_1_3_2_56_2","article-title":"Graph augmentation empowered contrastive learning for recommendation","author":"Xu Lixiang","year":"2024","unstructured":"Lixiang Xu, Yusheng Liu, Tong Xu, Enhong Chen, and Yuanyan Tang. 2024. Graph augmentation empowered contrastive learning for recommendation. ACM Transactions on Information Systems 43, 2 Article No.: 34 (2024), 1\u201327.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539342"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10707-021-00439-w"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3663574"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532023"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532023"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-022-1184-8"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3610407"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3605356"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411954"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159671"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645661"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3638535"}],"container-title":["ACM Transactions on Recommender Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3749998","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T09:27:40Z","timestamp":1773566860000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3749998"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,5]]},"references-count":67,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,9,30]]}},"alternative-id":["10.1145\/3749998"],"URL":"https:\/\/doi.org\/10.1145\/3749998","relation":{},"ISSN":["2770-6699"],"issn-type":[{"value":"2770-6699","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,5]]},"assertion":[{"value":"2025-02-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-29","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}