{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T19:52:24Z","timestamp":1772481144685,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"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":"crossref","award":["62461160311"],"award-info":[{"award-number":["62461160311"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s10115-026-02700-w","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T18:48:55Z","timestamp":1772477335000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sample enrichment via temporary operations on subsequences for sequential recommendation"],"prefix":"10.1007","volume":"68","author":[{"given":"Shu","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinwei","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weike","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangxing","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongcheng","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Ming","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"key":"2700_CR1","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2016) Session-based Recommendations with Recurrent Neural Networks"},{"key":"2700_CR2","doi-asserted-by":"crossref","unstructured":"Wu C-Y, Ahmed A, Beutel A, Smola AJ, Jing H (2017) Recurrent recommender networks. In: Proceedings of the 10th ACM International Conference on Web Search and Data Mining, pp. 495\u2013503","DOI":"10.1145\/3018661.3018689"},{"issue":"2","key":"2700_CR3","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1109\/TKDE.2018.2881260","volume":"32","author":"Q Cui","year":"2018","unstructured":"Cui Q, Wu S, Liu Q, Zhong W, Wang L (2018) MV-RNN: A multi-view recurrent neural network for sequential recommendation. IEEE Trans Knowl Data Eng 32(2):317\u2013331","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2700_CR4","doi-asserted-by":"crossref","unstructured":"Tang J, Wang K (2018) Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining, pp. 565\u2013573","DOI":"10.1145\/3159652.3159656"},{"key":"2700_CR5","doi-asserted-by":"crossref","unstructured":"Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T (2019) Session-based recommendation with graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 346\u2013353","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"2700_CR6","doi-asserted-by":"crossref","unstructured":"Yang L, Wang S, Tao Y, Sun J, Liu X, Yu PS, Wang T (2023) DGRec: Graph neural network for recommendation with diversified embedding generation. In: Proceedings of the 16th ACM International Conference on Web Search and Data Mining, pp. 661\u2013669","DOI":"10.1145\/3539597.3570472"},{"key":"2700_CR7","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhao Y, Zhang Y, Derr T (2023) Collaboration-aware graph convolutional network for recommender systems. In: Proceedings of the ACM Web Conference 2023, pp. 91\u2013101","DOI":"10.1145\/3543507.3583229"},{"key":"2700_CR8","doi-asserted-by":"crossref","unstructured":"Kang W-C, McAuley J (2018) Self-attentive sequential recommendation. In: Proceedings of 2018 IEEE 18th International Conference on Data Mining, pp. 197\u2013206","DOI":"10.1109\/ICDM.2018.00035"},{"key":"2700_CR9","doi-asserted-by":"crossref","unstructured":"Lin J, Pan W, Ming Z (2020) Fissa: Fusing item similarity models with self-attention networks for sequential recommendation. In: Proceedings of the 14th ACM Conference on Recommender Systems, pp. 130\u2013139","DOI":"10.1145\/3383313.3412247"},{"key":"2700_CR10","doi-asserted-by":"crossref","unstructured":"Zhou K, Wang H, Zhao WX, Zhu Y, Wang S, Zhang F, Wang Z, Wen J-R (2020) S3-Rec: Self-supervised learning for sequential recommendation with mutual information maximization. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management, pp. 1893\u20131902","DOI":"10.1145\/3340531.3411954"},{"key":"2700_CR11","doi-asserted-by":"crossref","unstructured":"Xu Z, Pan W, Ming Z (2023) A multi-view graph contrastive learning framework for cross-domain sequential recommendation. In: Proceedings of the 17th ACM Conference on Recommender Systems, pp. 491\u2013501","DOI":"10.1145\/3604915.3608785"},{"key":"2700_CR12","doi-asserted-by":"crossref","unstructured":"Wang K, Zhu Y, Zang T, Wang C, Jing M (2024) Enhanced hierarchical contrastive learning for recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 9107\u20139115","DOI":"10.1609\/aaai.v38i8.28761"},{"key":"2700_CR13","doi-asserted-by":"crossref","unstructured":"Zhou K, Yu H, Zhao WX, Wen J-R (2022) Filter-enhanced mlp is all you need for sequential recommendation. In: Proceedings of the ACM Web Conference 2022, pp. 2388\u20132399","DOI":"10.1145\/3485447.3512111"},{"key":"2700_CR14","unstructured":"Zimdars A, Chickering DM, Meek C (2013) Using Temporal Data for Making Recommendations"},{"key":"2700_CR15","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, pp. 452\u2013461"},{"key":"2700_CR16","doi-asserted-by":"crossref","unstructured":"He R, Kang W-C, McAuley J (2017) Translation-based recommendation. In: Proceedings of the 11th ACM Conference on Recommender Systems, pp. 161\u2013169","DOI":"10.1145\/3109859.3109882"},{"key":"2700_CR17","doi-asserted-by":"crossref","unstructured":"Li H, Liu Y, Mamoulis N, Rosenblum DS (2020) Translation-based sequential recommendation for complex users on sparse data. IEEE Transactions on Knowledge and Data Engineering, 1639\u20131651","DOI":"10.1109\/TKDE.2019.2906180"},{"key":"2700_CR18","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Lu, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 30th Annual Conference on Neural Information Processing Systems, pp. 5998\u20136008"},{"key":"2700_CR19","doi-asserted-by":"crossref","unstructured":"Jiang J, Zhang P, Luo Y, Li C, Kim JB, Zhang K, Wang S, Xie X, Kim S (2023) AdaMCT: Adaptive mixture of cnn-transformer for sequential recommendation. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 976\u2013986","DOI":"10.1145\/3583780.3614773"},{"key":"2700_CR20","doi-asserted-by":"crossref","unstructured":"Pancha N, Zhai A, Leskovec J, Rosenberg C (2022) Pinnerformer: Sequence modeling for user representation at pinterest. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 3702\u20133712","DOI":"10.1145\/3534678.3539156"},{"key":"2700_CR21","unstructured":"Zhai J, Liao L, Liu X, Wang Y, Li R, Cao X, Gao L, Gong Z, Gu F, He M, Lu Y, Shi Y (2024) Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations"},{"key":"2700_CR22","doi-asserted-by":"crossref","unstructured":"Zhou P, Huang Y-L, Xie Y, Gao J, Wang S, Kim JB, Kim S (2024) Is contrastive learning necessary? a study of data augmentation vs contrastive learning in sequential recommendation. In: Proceedings of the ACM Web Conference 2024, pp. 3854\u20133863","DOI":"10.1145\/3589334.3645661"},{"key":"2700_CR23","doi-asserted-by":"crossref","unstructured":"Zhang J, Xue R, Fan W, Xu X, Li Q, Pei J, Liu X (2024) Linear-time graph neural networks for scalable recommendations. In: Proceedings of the ACM on Web Conference 2024, pp. 3533\u20133544","DOI":"10.1145\/3589334.3645486"},{"key":"2700_CR24","doi-asserted-by":"crossref","unstructured":"Wang S, Cao L, Wang Y, Sheng QZ, Orgun MA, Lian D (2022) A survey on session-based recommender systems. ACM Computing Surveys, 154\u2013115438","DOI":"10.1145\/3465401"},{"key":"2700_CR25","unstructured":"Chen S, Xu Z, Pan W, Yang Q, Ming Z (2024) A survey on cross-domain sequential recommendation. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence, pp. 7989\u20137998"},{"key":"2700_CR26","doi-asserted-by":"crossref","unstructured":"Ma M, Ren P, Lin Y, Chen Z, Ma J, Rijke Md (2019) $$\\pi $$-net: A parallel information-sharing network for shared-account cross-domain sequential recommendations. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 685\u2013694","DOI":"10.1145\/3331184.3331200"},{"key":"2700_CR27","doi-asserted-by":"crossref","unstructured":"Alharbi N, Caragea D (2022) Cross-domain self-attentive sequential recommendations. In: Proceedings of International Conference on Data Science and Applications, pp. 601\u2013614","DOI":"10.1007\/978-981-16-5348-3_48"},{"key":"2700_CR28","doi-asserted-by":"crossref","unstructured":"Ye X, Li Y, Yao L (2023) Dream: Decoupled representation via extraction attention module and supervised contrastive learning for cross-domain sequential recommender. In: Proceedings of the 17th ACM Conference on Recommender Systems, pp. 479\u2013490","DOI":"10.1145\/3604915.3608780"},{"key":"2700_CR29","doi-asserted-by":"publisher","first-page":"120550","DOI":"10.1016\/j.ins.2024.120550","volume":"669","author":"Z Xu","year":"2024","unstructured":"Xu Z, Pan W, Ming Z (2024) Transfer learning in cross-domain sequential recommendation. Inf Sci 669:120550","journal-title":"Inf Sci"},{"key":"2700_CR30","doi-asserted-by":"crossref","unstructured":"Xu W, Wu Q, Wang R, Ha M, Ma Q, Chen L, Han B, Yan J (2024) Rethinking cross-domain sequential recommendation under open-world assumptions. In: Proceedings of the ACM Web Conference 2024","DOI":"10.1145\/3589334.3645351"},{"key":"2700_CR31","doi-asserted-by":"crossref","unstructured":"Petrov A, Macdonald C (2023) Rss: Effective and efficient training for sequential recommendation using recency sampling. ACM Transactions on Recommender Systems","DOI":"10.1145\/3523227.3546785"},{"key":"2700_CR32","doi-asserted-by":"crossref","unstructured":"Qiu R, Huang Z, Yin H, Wang Z (2022) Contrastive learning for representation degeneration problem in sequential recommendation. In: Proceedings of the 15th ACM International Conference on Web Search and Data Mining, pp. 813\u2013823","DOI":"10.1145\/3488560.3498433"},{"key":"2700_CR33","doi-asserted-by":"crossref","unstructured":"Liu Q, Yan F, Zhao X, Du Z, Guo H, Tang R, Tian F (2023) Diffusion augmentation for sequential recommendation. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 1576\u20131586","DOI":"10.1145\/3583780.3615134"},{"key":"2700_CR34","doi-asserted-by":"crossref","unstructured":"Xie X, Sun F, Liu Z, Wu S, Gao J, Zhang J, Ding B, Cui B (2022) Contrastive learning for sequential recommendation. In: 2022 IEEE 38th International Conference on Data Engineering, pp. 1259\u20131273","DOI":"10.1109\/ICDE53745.2022.00099"},{"key":"2700_CR35","doi-asserted-by":"crossref","unstructured":"Dang Y, Yang E, Guo G, Jiang L, Wang X, Xu X, Sun Q, Liu H (2023) Uniform sequence better: Time interval aware data augmentation for sequential recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 4225\u20134232","DOI":"10.1609\/aaai.v37i4.25540"},{"key":"2700_CR36","doi-asserted-by":"crossref","unstructured":"Cao Y, Zhou X, Feng J, Huang P, Xiao Y, Chen D, Chen S (2022) Sampling is all you need on modeling long-term user behaviors for ctr prediction. In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management, pp. 2974\u20132983","DOI":"10.1145\/3511808.3557082"},{"key":"2700_CR37","doi-asserted-by":"crossref","unstructured":"Pi Q, Zhou G, Zhang Y, Wang Z, Ren L, Fan Y, Zhu X, Gai K (2020) Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management, pp. 2685\u20132692","DOI":"10.1145\/3340531.3412744"},{"key":"2700_CR38","doi-asserted-by":"crossref","unstructured":"Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 811\u2013820","DOI":"10.1145\/1772690.1772773"},{"key":"2700_CR39","doi-asserted-by":"crossref","unstructured":"He R, McAuley J (2016) Fusing similarity models with markov chains for sparse sequential recommendation. In: Proceedings of 2016 IEEE 16th International Conference on Data Mining, pp. 191\u2013200","DOI":"10.1109\/ICDM.2016.0030"},{"key":"2700_CR40","doi-asserted-by":"crossref","unstructured":"Sun F, Liu J, Wu J, Pei C, Lin X, Ou W, Jiang P (2019) Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441\u20131450","DOI":"10.1145\/3357384.3357895"},{"issue":"3","key":"2700_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3631116","volume":"42","author":"Z Li","year":"2023","unstructured":"Li Z, Sun A, Li C (2023) DiffuRec: A diffusion model for sequential recommendation. ACM Trans Inf Syst 42(3):1\u201328","journal-title":"ACM Trans Inf Syst"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-026-02700-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-026-02700-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-026-02700-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T18:49:06Z","timestamp":1772477346000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-026-02700-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,2]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["2700"],"URL":"https:\/\/doi.org\/10.1007\/s10115-026-02700-w","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,2]]},"assertion":[{"value":"5 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"89"}}