{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:16:02Z","timestamp":1784178962187,"version":"3.55.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Front. Comput. Sci."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s11704-023-3112-y","type":"journal-article","created":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T03:02:11Z","timestamp":1703732531000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A general tail item representation enhancement framework for sequential recommendation"],"prefix":"10.1007","volume":"18","author":[{"given":"Mingyue","family":"Cheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiding","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongke","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,28]]},"reference":[{"key":"3112_CR1","doi-asserted-by":"crossref","unstructured":"Wang S, Hu L, Wang Y, Cao L, Sheng Q Z, Orgun M A. Sequential recommender systems: challenges, progress and prospects. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019, 6332\u20136338","DOI":"10.24963\/ijcai.2019\/883"},{"key":"3112_CR2","doi-asserted-by":"crossref","unstructured":"Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T. Session-based recommendation with graph neural networks. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence and 31st Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. 2019, 43","DOI":"10.1609\/aaai.v33i01.3301346"},{"issue":"5","key":"3112_CR3","doi-asserted-by":"publisher","first-page":"175612","DOI":"10.1007\/s11704-022-2223-1","volume":"17","author":"E Xu","year":"2023","unstructured":"Xu E, Yu Z, Li N, Cui H, Yao L, Guo B. Quantifying predictability of sequential recommendation via logical constraints. Frontiers of Computer Science, 2023, 17(5): 175612","journal-title":"Frontiers of Computer Science"},{"key":"3112_CR4","doi-asserted-by":"crossref","unstructured":"Zhaok X, Liu H, Fan W, Liu H, Tang J, Wang C, Chen M, Zheng X, Liu X, Yang X. AutoEmb: automated embedding dimensionality search in streaming recommendations. In: Proceedings of 2021 IEEE International Conference on Data Mining. 2021, 896\u2013905","DOI":"10.1109\/ICDM51629.2021.00101"},{"key":"3112_CR5","doi-asserted-by":"crossref","unstructured":"Cheng M, Liu Q, Liu Z, Li Z, Luo Y, Chen E. FormerTime: hierarchical multi-scale representations for multivariate time series classification. In: Proceedings of the ACM Web Conference. 2023, 1437\u20131445","DOI":"10.1145\/3543507.3583205"},{"key":"3112_CR6","unstructured":"Cheng M, Liu Q, Liu Z, Zhang H, Zhang R, Chen E. TimeMAE: self-supervised representations of time series with decoupled masked autoencoders. 2023, arXiv preprint arXiv: 2303.00320"},{"key":"3112_CR7","doi-asserted-by":"crossref","unstructured":"Sun Y, Yuan F, Yang M, Wei G, Zhao Z, Liu D. A generic network compression framework for sequential recommender systems. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, 1299\u20131308","DOI":"10.1145\/3397271.3401125"},{"key":"3112_CR8","doi-asserted-by":"crossref","unstructured":"Chen L, Yuan F, Yang J, Ao X, Li C, Yang M. A user-adaptive layer selection framework for very deep sequential recommender models. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 3984\u20133991","DOI":"10.1609\/aaai.v35i5.16518"},{"key":"3112_CR9","doi-asserted-by":"crossref","unstructured":"Zhang S, Yao D, Zhao Z, Chua T S, Wu F. CauseRec: counterfactual user sequence synthesis for sequential recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021, 367\u2013377","DOI":"10.1145\/3404835.3462908"},{"key":"3112_CR10","doi-asserted-by":"crossref","unstructured":"Yin J, Liu C, Wang W, Sun J, Hoi S C H. Learning transferrable parameters for long-tailed sequential user behavior modeling. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020, 359\u2013367","DOI":"10.1145\/3394486.3403078"},{"key":"3112_CR11","doi-asserted-by":"crossref","unstructured":"Kim Y, Kim K, Park C, Yu H. Sequential and diverse recommendation with long tail. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019, 2740\u20132746","DOI":"10.24963\/ijcai.2019\/380"},{"key":"3112_CR12","doi-asserted-by":"crossref","unstructured":"Fan Z, Liu Z, Zhang J, Xiong Y, Zheng L, Yu P S. Continuous-time sequential recommendation with temporal graph collaborative transformer. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021, 433\u2013442","DOI":"10.1145\/3459637.3482242"},{"key":"3112_CR13","volume-title":"Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology","author":"G K Zipf","year":"2016","unstructured":"Zipf G K. Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology. Xue C F, trans. Shanghai: Shanghai People\u2019s Publishing House, 2016"},{"issue":"9","key":"3112_CR14","doi-asserted-by":"publisher","first-page":"896","DOI":"10.14778\/2311906.2311916","volume":"5","author":"H Yin","year":"2012","unstructured":"Yin H, Cui B, Li J, Yao J, Chen C. Challenging the long tail recommendation. Proceedings of the VLDB Endowment, 2012, 5(9): 896\u2013907","journal-title":"Proceedings of the VLDB Endowment"},{"key":"3112_CR15","doi-asserted-by":"crossref","unstructured":"Liu Z, Cheng M, Li Z, Liu Q, Chen E. One person, one model-learning compound router for sequential recommendation. In: Proceedings of IEEE International Conference on Data Mining. 2022, 289\u2013298","DOI":"10.1109\/ICDM54844.2022.00039"},{"key":"3112_CR16","doi-asserted-by":"crossref","unstructured":"Wei T, Feng F, Chen J, Wu Z, Yi J, He X. Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021, 1791\u20131800","DOI":"10.1145\/3447548.3467289"},{"key":"3112_CR17","doi-asserted-by":"crossref","unstructured":"Liu S, Zheng Y. Long-tail session-based recommendation. In: Proceedings of the 14th ACM Conference on Recommender Systems. 2020, 509\u2013514","DOI":"10.1145\/3383313.3412222"},{"key":"3112_CR18","doi-asserted-by":"crossref","unstructured":"Jang S, Lee H, Cho H, Chung S. CITIES: contextual inference of tail-item embeddings for sequential recommendation. In: Proceedings of the 20th IEEE International Conference on Data Mining. 2020, 202\u2013211","DOI":"10.1109\/ICDM50108.2020.00029"},{"key":"3112_CR19","doi-asserted-by":"crossref","unstructured":"Kang W C, McAuley J. Self-attentive sequential recommendation. In: Proceedings of 2018 IEEE International Conference on Data Mining. 2018, 197\u2013206","DOI":"10.1109\/ICDM.2018.00035"},{"key":"3112_CR20","doi-asserted-by":"crossref","unstructured":"Rendle S, Freudenthaler C, Schmidt-Thieme L. Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web. 2010, 811\u2013820","DOI":"10.1145\/1772690.1772773"},{"key":"3112_CR21","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, Tikk D. Session-based recommendations with recurrent neural networks. In: Proceedings of the 4th International Conference on Learning Representations. 2016"},{"key":"3112_CR22","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 6000\u20136010"},{"key":"3112_CR23","doi-asserted-by":"crossref","unstructured":"Cheng M, Liu Z, Liu Q, Ge S, Chen E. Towards automatic discovering of deep hybrid network architecture for sequential recommendation. In: Proceedings of the ACM Web Conference 2022. 2022, 1923\u20131932","DOI":"10.1145\/3485447.3512066"},{"key":"3112_CR24","doi-asserted-by":"crossref","unstructured":"Sun F, Liu J, Wu J, Pei C, Lin X, Ou W, Jiang P. BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019, 1441\u20131450","DOI":"10.1145\/3357384.3357895"},{"key":"3112_CR25","doi-asserted-by":"crossref","unstructured":"Cheng M, Yuan F, Liu Q, Ge S, Li Z, Yu R, Lian D, Yuan S, Chen E. Learning recommender systems with implicit feedback via soft target enhancement. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021, 575\u2013584","DOI":"10.1145\/3404835.3462863"},{"issue":"3","key":"3112_CR26","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1145\/3314578","volume":"37","author":"F Xue","year":"2019","unstructured":"Xue F, He X, Wang X, Xu J, Liu K, Hong R. Deep item-based collaborative filtering for top-N recommendation. ACM Transactions on Information Systems, 2019, 37(3): 33","journal-title":"ACM Transactions on Information Systems"},{"key":"3112_CR27","unstructured":"Cai Y, Cui Z, Wu S, Lei Z, Ma X. Represent items by items: An enhanced representation of the target item for recommendation. 2021, arXiv preprint arXiv: 2104.12483"},{"key":"3112_CR28","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick R. Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2020, 9726\u20139735","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"3112_CR29","doi-asserted-by":"crossref","unstructured":"Liu Q, Zeng Y, Mokhosi R, Zhang H. STAMP: short-term attention\/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018, 1831\u20131839","DOI":"10.1145\/3219819.3219950"},{"key":"3112_CR30","doi-asserted-by":"crossref","unstructured":"De Souza Pereira Moreira G, Rabhi S, Lee J M, Ak R, Oldridge E. Transformers4Rec: bridging the gap between NLP and sequential\/session-based recommendation. In: Proceedings of the 15th ACM Conference on Recommender Systems. 2021, 143\u2013153","DOI":"10.1145\/3460231.3474255"},{"key":"3112_CR31","doi-asserted-by":"crossref","unstructured":"Tang J, Wang K. Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 2018, 565\u2013573","DOI":"10.1145\/3159652.3159656"},{"key":"3112_CR32","doi-asserted-by":"crossref","unstructured":"Cheng M, Yuan F, Liu Q, Xin X, Chen E. Learning transferable user representations with sequential behaviors via contrastive pre-training. In: Proceedings of 2021 IEEE International Conference on Data Mining (ICDM). 2021, 51\u201360","DOI":"10.1109\/ICDM51629.2021.00015"},{"key":"3112_CR33","doi-asserted-by":"crossref","unstructured":"Li J, Ren P, Chen Z, Ren Z, Lian T, Ma J. Neural attentive session-based recommendation. In: Proceedings of 2017 ACM on Conference on Information and Knowledge Management. 2017, 1419\u20131428","DOI":"10.1145\/3132847.3132926"},{"key":"3112_CR34","doi-asserted-by":"crossref","unstructured":"Yuan F, Karatzoglou A, Arapakis I, Jose J M, He X. A simple convolutional generative network for next item recommendation. In: Proceedings of the 12th ACM International Conference on Web Search and Data Mining. 2019, 582\u2013590","DOI":"10.1145\/3289600.3290975"},{"key":"3112_CR35","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 2012, 452\u2013461"},{"key":"3112_CR36","doi-asserted-by":"crossref","unstructured":"Zhang Y, Cheng D Z, Yao T, Yi X, Hong L, Chi E H. A model of two tales: dual transfer learning framework for improved long-tail item recommendation. In: Proceedings of the Web Conference 2021. 2021, 2220\u20132231","DOI":"10.1145\/3442381.3450086"},{"key":"3112_CR37","doi-asserted-by":"publisher","unstructured":"Zhao Z, Chen J, Zhou S, He X, Cao X, Zhang F, Wu W. Popularity bias is not always evil: disentangling benign and harmful bias for recommendation. IEEE Transactions on Knowledge and Data Engineering, 2022, doi: https:\/\/doi.org\/10.1109\/TKDE.2022.3218994","DOI":"10.1109\/TKDE.2022.3218994"},{"key":"3112_CR38","doi-asserted-by":"crossref","unstructured":"He X, Chua T S. Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017, 355\u2013364","DOI":"10.1145\/3077136.3080777"},{"key":"3112_CR39","doi-asserted-by":"crossref","unstructured":"Pi Q, Zhou G, Zhang Y, Wang Z, Ren L, Fan Y, Zhu X, Gai K. 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 & Knowledge Management. 2020, 2685\u20132692","DOI":"10.1145\/3340531.3412744"},{"key":"3112_CR40","doi-asserted-by":"crossref","unstructured":"He R, Fang C, Wang Z, McAuley J. Vista: a visually, socially, and temporally-aware model for artistic recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems. 2016, 309\u2013316","DOI":"10.1145\/2959100.2959152"},{"key":"3112_CR41","doi-asserted-by":"crossref","unstructured":"Ying H, Zhuang F, Zhang F, Liu Y, Xu G, Xie X, Xiong H, Wu J. Sequential recommender system based on hierarchical attention network. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2018, 3926\u20133932","DOI":"10.24963\/ijcai.2018\/546"},{"key":"3112_CR42","doi-asserted-by":"crossref","unstructured":"Gu Y, Lei T, Barzilay R, Jaakkola T. Learning to refine text based recommendations. In: Proceedings of 2016 Conference on Empirical Methods in Natural Language Processing. 2016, 2103\u20132108","DOI":"10.18653\/v1\/D16-1227"},{"key":"3112_CR43","doi-asserted-by":"crossref","unstructured":"Tan Y K, Xu X, Liu Y. Improved recurrent neural networks for session-based recommendations. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 2016, 17\u201322","DOI":"10.1145\/2988450.2988452"},{"key":"3112_CR44","doi-asserted-by":"crossref","unstructured":"Huang J, Zhao W X, Dou H, Wen J R, Chang E Y. Improving sequential recommendation with knowledge-enhanced memory networks. In: Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018, 505\u2013514","DOI":"10.1145\/3209978.3210017"},{"key":"3112_CR45","doi-asserted-by":"crossref","unstructured":"Wang J, Yuan F, Chen J, Wu Q, Yang M, Sun Y, Zhang G. StackRec: efficient training of very deep sequential recommender models by iterative stacking. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021, 357\u2013366","DOI":"10.1145\/3404835.3462890"},{"issue":"6","key":"3112_CR46","doi-asserted-by":"publisher","first-page":"176613","DOI":"10.1007\/s11704-022-2324-x","volume":"17","author":"Y Liang","year":"2023","unstructured":"Liang Y, Song Q, Zhao Z, Zhou H, Gong M. BA-GNN: Behavior-aware graph neural network for session-based recommendation. Frontiers of Computer Science, 2023, 17(6): 176613","journal-title":"Frontiers of Computer Science"},{"issue":"4","key":"3112_CR47","first-page":"67","volume":"47","author":"E Brynjolfsson","year":"2006","unstructured":"Brynjolfsson E, Hu Y J, Smith M D. From niches to riches: anatomy of the long tail. Sloan Management Review, 2006, 47(4): 67\u201371","journal-title":"Sloan Management Review"},{"key":"3112_CR48","unstructured":"Liang D, Charlin L, Blei D M. Causal inference for recommendation. In: Proceedings of Causation: Foundation to Application, Workshop at UAI. 2016"},{"key":"3112_CR49","doi-asserted-by":"crossref","unstructured":"Abdollahpouri H, Burke R, Mobasher B. Controlling popularity bias in learning-to-rank recommendation. In: Proceedings of the 11th ACM Conference on Recommender Systems. 2017, 42\u201346","DOI":"10.1145\/3109859.3109912"},{"issue":"5","key":"3112_CR50","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1109\/TKDE.2011.15","volume":"24","author":"G Adomavicius","year":"2012","unstructured":"Adomavicius G, Kwon Y. Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(5): 896\u2013911","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"1","key":"3112_CR51","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1109\/TSC.2017.2681666","volume":"13","author":"B Bai","year":"2020","unstructured":"Bai B, Fan Y, Tan W, Zhang J. DLTSR: a deep learning framework for recommendations of long-tail web services. IEEE Transactions on Services Computing, 2020, 13(1): 73\u201385","journal-title":"IEEE Transactions on Services Computing"},{"key":"3112_CR52","doi-asserted-by":"crossref","unstructured":"Li J, Lu K, Huang Z, Shen H T. Two birds one stone: on both cold-start and long-tail recommendation. In: Proceedings of the 25th ACM International Conference on Multimedia. 2017, 898\u2013906","DOI":"10.1145\/3123266.3123316"}],"container-title":["Frontiers of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-023-3112-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11704-023-3112-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-023-3112-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T20:32:58Z","timestamp":1768854778000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11704-023-3112-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,28]]},"references-count":52,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["3112"],"URL":"https:\/\/doi.org\/10.1007\/s11704-023-3112-y","relation":{},"ISSN":["2095-2228","2095-2236"],"issn-type":[{"value":"2095-2228","type":"print"},{"value":"2095-2236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,28]]},"assertion":[{"value":"7 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Competing interests\n                      The authors declare that they have no competing interests or financial conflicts to disclose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"186333"}}