{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:29:57Z","timestamp":1784179797232,"version":"3.55.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LY24F020013"],"award-info":[{"award-number":["LY24F020013"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s10618-026-01185-z","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T12:00:39Z","timestamp":1771243239000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-faceted, multi-scale, and multi-task trend learning for denied check-in prediction on online travel platforms"],"prefix":"10.1007","volume":"40","author":[{"given":"Fanwei","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zulong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanjie","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailong","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zui","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,16]]},"reference":[{"issue":"10","key":"1185_CR1","doi-asserted-by":"publisher","first-page":"1713","DOI":"10.3390\/pr9101713","volume":"9","author":"M Adil","year":"2021","unstructured":"Adil M, Ansari MF, Alahmadi AA et al (2021) Solving the problem of class imbalance in the prediction of hotel cancelations: a hybridized machine learning approach. Processes 9(10):1713","journal-title":"Processes"},{"issue":"5","key":"1185_CR2","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1177\/1354816618801741","volume":"25","author":"A Ampountolas","year":"2019","unstructured":"Ampountolas A (2019) Forecasting hotel demand uncertainty using time series bayesian var models. Tour Econ 25(5):734\u2013756","journal-title":"Tour Econ"},{"issue":"2","key":"1185_CR3","doi-asserted-by":"publisher","first-page":"25","DOI":"10.18089\/tms.2017.13203","volume":"13","author":"N Ant\u00f3nio","year":"2017","unstructured":"Ant\u00f3nio N, De Almeida A, Nunes L (2017) Predicting hotel booking cancellations to decrease uncertainty and increase revenue. Tour Manag Stud 13(2):25\u201339","journal-title":"Tour Manag Stud"},{"key":"1185_CR4","doi-asserted-by":"publisher","first-page":"32","DOI":"10.5334\/dsj-2019-032","volume":"18","author":"N Antonio","year":"2019","unstructured":"Antonio N, de Almeida A, Nunes L (2019) An automated machine learning based decision support system to predict hotel booking cancellations. Data Sci J 18:32\u201332","journal-title":"Data Sci J"},{"issue":"5","key":"1185_CR5","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1057\/s41272-020-00268-w","volume":"20","author":"F Binesh","year":"2021","unstructured":"Binesh F, Belarmino A, Raab C (2021) A meta-analysis of hotel revenue management. J Revenue Pricing Manag 20(5):546\u2013558","journal-title":"J Revenue Pricing Manag"},{"key":"1185_CR6","unstructured":"Chen T, He T, Benesty M, et\u00a0al (2015) Xgboost: extreme gradient boosting. R package version 04-2 1(4):1\u20134"},{"issue":"9","key":"1185_CR7","doi-asserted-by":"publisher","first-page":"11696","DOI":"10.3390\/su70911696","volume":"7","author":"Y Dong","year":"2015","unstructured":"Dong Y, Ling L (2015) Hotel overbooking and cooperation with third-party websites. Sustainability 7(9):11696\u201311712","journal-title":"Sustainability"},{"issue":"1","key":"1185_CR8","first-page":"132","volume":"25","author":"MC Enache","year":"2019","unstructured":"Enache MC (2019) Machine learning in tourism revenue management. Annals of Dunarea De Jos University Fascicle I Economics and Applied Informatics 25(1):132\u2013136","journal-title":"Annals Dunarea De Jos Univ Fascicle I Econ Appl Inf"},{"key":"1185_CR9","unstructured":"Expedia (2025) https:\/\/www.expedia.com, a leading global online travel platform"},{"issue":"1","key":"1185_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3426723","volume":"39","author":"H Fang","year":"2020","unstructured":"Fang H, Zhang D, Shu Y et al (2020) Deep learning for sequential recommendation: algorithms, influential factors, and evaluations. ACM Trans Inf Syst 39(1):1\u201342. https:\/\/doi.org\/10.1145\/3426723","journal-title":"ACM Trans Inf Syst"},{"key":"1185_CR11","unstructured":"Fliggy (2025) http:\/\/www.fliggy.com, one of the most popular online travel platforms in China"},{"issue":"4","key":"1185_CR12","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","volume":"38","author":"JH Friedman","year":"2002","unstructured":"Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367\u2013378","journal-title":"Comput Stat Data Anal"},{"key":"1185_CR13","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Talal I, Azizsoltani M, Tal\u00f3n-Ballestero P, et\u00a0al (2025) Machine learning in hospitality: interpretable forecasting of booking cancellations. IEEE Access","DOI":"10.1109\/ACCESS.2025.3536094"},{"key":"1185_CR14","unstructured":"Gu A, Dao T (2024) Mamba: linear-time sequence modeling with selective state spaces. In: First conference on language modeling"},{"issue":"8","key":"1185_CR15","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"1185_CR16","doi-asserted-by":"crossref","unstructured":"Hsieh TY, Wang S, Sun Y, et\u00a0al (2021) Explainable multivariate time series classification: a deep neural network which learns to attend to important variables as well as time intervals. In: Proceedings of the 14th ACM International conference on web search and data mining, pp 607\u2013615","DOI":"10.1145\/3437963.3441815"},{"key":"1185_CR17","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.ijpe.2004.06.038","volume":"93\u201394","author":"T Koide","year":"2005","unstructured":"Koide T, Ishii H (2005) The hotel yield management with two types of room prices, overbooking and cancellations. Int J Prod Econ 93\u201394:417\u2013428","journal-title":"Int J Prod Econ"},{"key":"1185_CR18","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1287\/msom.1070.0169","volume":"10","author":"Q Liu","year":"2008","unstructured":"Liu Q, van Ryzin GJ (2008) On the choice-based linear programming model for network revenue management. Manuf Serv Oper Manag 10:288\u2013310","journal-title":"Manuf Serv Oper Manag"},{"issue":"3","key":"1185_CR19","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1177\/1096348010382238","volume":"35","author":"BM Noone","year":"2011","unstructured":"Noone BM, Lee CH (2011) Hotel overbooking: the effect of overcompensation on customers\u2019 reactions to denied service. J Hosp Tour Res 35(3):334\u2013357","journal-title":"J Hosp Tour Res"},{"key":"1185_CR20","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1177\/0047287516669050","volume":"56","author":"B Pan","year":"2017","unstructured":"Pan B, Yang Y (2017) Forecasting destination weekly hotel occupancy with big data. J Travel Res 56:957\u2013970","journal-title":"J Travel Res"},{"key":"1185_CR21","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1057\/s41272-021-00363-6","volume":"22","author":"N Phumchusri","year":"2021","unstructured":"Phumchusri N, Suwatanapongched P (2021) Forecasting hotel daily room demand with transformed data using time series methods. J Revenue Pricing Manag 22:44\u201356","journal-title":"J Revenue Pricing Manag"},{"issue":"7","key":"1185_CR22","first-page":"579","volume":"8","author":"MC Popescu","year":"2009","unstructured":"Popescu MC, Balas VE, Perescu-Popescu L et al (2009) Multilayer perceptron and neural networks. WSEAS transactions on circuits and systems 8(7):579\u2013588","journal-title":"WSEAS Trans Circuits Syst"},{"key":"1185_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijhm.2019.03.018","volume":"82","author":"A Riasi","year":"2019","unstructured":"Riasi A, Schwartz Z, Beldona S (2019) Hotel overbooking taxonomy: who and how? Int J Hosp Manag 82:1\u20134","journal-title":"Int J Hosp Manag"},{"issue":"3","key":"1185_CR24","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1016\/j.ijforecast.2019.07.001","volume":"36","author":"D Salinas","year":"2020","unstructured":"Salinas D, Flunkert V, Gasthaus J et al (2020) Deepar: probabilistic forecasting with autoregressive recurrent networks. Int J Forecast 36(3):1181\u20131191","journal-title":"Int J Forecast"},{"key":"1185_CR25","doi-asserted-by":"crossref","unstructured":"Satu MS, Ahammed K, Abedin MZ (2020) Performance analysis of machine learning techniques to predict hotel booking cancellations in hospitality industry. 23rd International conference on computer and information technology (ICCIT)","DOI":"10.1109\/ICCIT51783.2020.9392648"},{"key":"1185_CR26","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.ejor.2015.04.014","volume":"246","author":"DD Sierag","year":"2015","unstructured":"Sierag DD, Koole G, van der Mei RD et al (2015) Revenue management under customer choice behaviour with cancellations and overbooking. Eur J Oper Res 246:170\u2013185","journal-title":"Eur J Oper Res"},{"issue":"2","key":"1185_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3704998","volume":"43","author":"Y Sun","year":"2025","unstructured":"Sun Y, Ji Y, Zhu H et al (2025) Market-aware long-term job skill recommendation with explainable deep reinforcement learning. ACM Trans Inf Syst 43(2):1\u201335. https:\/\/doi.org\/10.1145\/3704998","journal-title":"ACM Trans Inf Syst"},{"key":"1185_CR28","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1287\/mnsc.1030.0147","volume":"50","author":"KT Talluri","year":"2004","unstructured":"Talluri KT, van Ryzin GJ (2004) Revenue management under a general discrete choice model of consumer behavior. Manag Sci 50:15\u201333","journal-title":"Manag Sci"},{"key":"1185_CR29","unstructured":"Tran X (2024) Strategies of revenue management. Revenue Manag Illus"},{"key":"1185_CR30","first-page":"1375","volume":"4","author":"GN Vajpai","year":"2018","unstructured":"Vajpai GN (2018) Managing overbooking in hotels: a probabilistic model using poisson distribution.Int J Adv Res Ideas Innov Technol 4:1375\u20131379","journal-title":"Int J Adv Res Ideas InnovTechnol"},{"key":"1185_CR31","unstructured":"Vaswani A, Shazeer N, Parmar N, et\u00a0al (2017) Attention is all you need. In: Advances in neural information processing systems (NIPS), pp 5998\u20136008"},{"key":"1185_CR32","doi-asserted-by":"crossref","unstructured":"Yin H, Qu L, Chen T, et\u00a0al (2025) On-device recommender systems: a comprehensive survey. Data Sci Eng pp 1\u201330","DOI":"10.1007\/s41019-025-00308-8"},{"issue":"7","key":"1185_CR33","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu Y, Si X, Hu C et al (2019) A review of recurrent neural networks: Lstm cells and network architectures. Neural Comput 31(7):1235\u20131270","journal-title":"Neural Comput"},{"key":"1185_CR34","unstructured":"Yuan W, Ye G, Zhao X, et\u00a0al (2024) Tackling data heterogeneity in federated time series forecasting. arXiv preprint arXiv:2411.15716"},{"key":"1185_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2023.109226","volume":"180","author":"Q Zhai","year":"2023","unstructured":"Zhai Q, Tian Y, Luo J et al (2023) Hotel overbooking based on no-show probability forecasts. Comput Ind Eng 180:109226","journal-title":"Comput Ind Eng"},{"key":"1185_CR36","unstructured":"Zhang Q, Wen H, Yuan W, et\u00a0al (2025) Hmamba: hyperbolic mamba for sequential recommendation. arXiv preprint arXiv:2505.09205"},{"key":"1185_CR37","doi-asserted-by":"crossref","unstructured":"Zhu F, Xiao W, Yu Y, et\u00a0al (2024) Dynamic hotel pricing at online travel platforms: a popularity and competitiveness aware demand learning approach. In: Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining, pp 4641\u20134651","DOI":"10.1145\/3637528.3671921"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-026-01185-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-026-01185-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-026-01185-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T04:53:36Z","timestamp":1774846416000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-026-01185-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,16]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["1185"],"URL":"https:\/\/doi.org\/10.1007\/s10618-026-01185-z","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,16]]},"assertion":[{"value":"17 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2026","order":3,"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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"18"}}