{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T20:38:21Z","timestamp":1780519101904,"version":"3.54.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"\u201cXingdian Talent Support Program\u201d Youth Talent Special Project of Yunnan Province","award":["XDYC-QNRC-2022-0141"],"award-info":[{"award-number":["XDYC-QNRC-2022-0141"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11227-024-06675-9","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T17:25:29Z","timestamp":1731605129000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Enhancing game customer churn prediction with a stacked ensemble learning model"],"prefix":"10.1007","volume":"81","author":[{"given":"Rui","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yungang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanfang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"6675_CR1","unstructured":"China Audio-Video GPC, Association DP (2023) The 2023 China Game Industry report officially released. China Game Industry Network. https:\/\/www.cgigc.com.cn\/details.html?id=08dc70a3-deb3-4af9-8043-8b92d80fff2c&tp=report. Accessed 14 Sept 2024"},{"key":"6675_CR2","unstructured":"China Audio-Video GPC, Association DP (2024) China Game Industry Group Committee\u2014Official Website of the Game Publishing Committee. https:\/\/www.cgigc.com.cn\/report.html. Accessed 14 Sept 2024"},{"issue":"3","key":"6675_CR3","doi-asserted-by":"publisher","first-page":"2403","DOI":"10.1007\/s11277-021-09362-7","volume":"126","author":"M Junaid","year":"2022","unstructured":"Junaid M, Ali S, Siddiqui IF, Nam C, Qureshi NMF, Kim J, Shin DR (2022) Performance evaluation of data-driven intelligent algorithms for big data ecosystem. Wirel Pers Commun 126(3):2403\u20132423. https:\/\/doi.org\/10.1007\/s11277-021-09362-7","journal-title":"Wirel Pers Commun"},{"issue":"3","key":"6675_CR4","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s41060-022-00312-5","volume":"14","author":"L Geiler","year":"2022","unstructured":"Geiler L, Affeldt S, Nadif M (2022) A survey on machine learning methods for churn prediction. Int J Data Sci Anal 14(3):217\u2013242. https:\/\/doi.org\/10.1007\/s41060-022-00312-5","journal-title":"Int J Data Sci Anal"},{"issue":"21","key":"6675_CR5","doi-asserted-by":"publisher","first-page":"4","DOI":"10.4108\/eetmca.v6i21.2181","volume":"6","author":"SO Abdulsalam","year":"2022","unstructured":"Abdulsalam SO, Ajao JF, Balogun BF, Arowolo MO (2022) A churn prediction system for telecommunication company using random forest and convolution neural network algorithms. ICST Trans Mob Commun Appl 6(21):4. https:\/\/doi.org\/10.4108\/eetmca.v6i21.2181","journal-title":"ICST Trans Mob Commun Appl"},{"issue":"6","key":"6675_CR6","doi-asserted-by":"publisher","first-page":"70","DOI":"10.29141\/2218-5003-2022-13-6-6","volume":"13","author":"K Arik","year":"2023","unstructured":"Arik K, Gezer M, Tayali ST (2023) The study of indicators affecting customer churn in MMORPG games with machine learning models. Upravlenets 13(6):70\u201385. https:\/\/doi.org\/10.29141\/2218-5003-2022-13-6-6","journal-title":"Upravlenets"},{"key":"6675_CR7","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/3-540-48229-6_9","volume-title":"Artificial Intelligence in Medicine. Series Title: Lecture Notes in Computer Science","author":"J Laurikkala","year":"2001","unstructured":"Laurikkala J (2001) Improving identification of difficult small classes by balancing class distribution. In: Goos G, Hartmanis J, Van Leeuwen J, Quaglini S, Barahona P, Andreassen S (eds) Artificial Intelligence in Medicine. Series Title: Lecture Notes in Computer Science, vol 2101. Springer, Berlin, pp 63\u201366. https:\/\/doi.org\/10.1007\/3-540-48229-6_9"},{"issue":"3","key":"6675_CR8","doi-asserted-by":"publisher","first-page":"94","DOI":"10.3390\/fi14030094","volume":"14","author":"T Zhang","year":"2022","unstructured":"Zhang T, Moro S, Ramos RF (2022) A data-driven approach to improve customer churn prediction based on telecom customer segmentation. Future Internet 14(3):94. https:\/\/doi.org\/10.3390\/fi14030094","journal-title":"Future Internet"},{"issue":"2","key":"6675_CR9","doi-asserted-by":"publisher","first-page":"458","DOI":"10.3390\/jtaer17020024","volume":"17","author":"X Xiahou","year":"2022","unstructured":"Xiahou X, Harada Y (2022) B2C E-commerce customer churn prediction based on K-means and SVM. J Theor Appl Electron Commer Res 17(2):458\u2013475. https:\/\/doi.org\/10.3390\/jtaer17020024","journal-title":"J Theor Appl Electron Commer Res"},{"issue":"3","key":"6675_CR10","doi-asserted-by":"publisher","first-page":"2007","DOI":"10.1007\/s12652-021-03413-4","volume":"14","author":"B Garimella","year":"2023","unstructured":"Garimella B, Prasad GVSNRV, Prasad MHMK (2023) Churn prediction using optimized deep learning classifier on huge telecom data. J Ambient Intell Humaniz Comput 14(3):2007\u20132028. https:\/\/doi.org\/10.1007\/s12652-021-03413-4","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"1","key":"6675_CR11","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s40537-019-0191-6","volume":"6","author":"AK Ahmad","year":"2019","unstructured":"Ahmad AK, Jafar A, Aljoumaa K (2019) Customer churn prediction in telecom using machine learning in big data platform. J Big Data 6(1):28. https:\/\/doi.org\/10.1186\/s40537-019-0191-6","journal-title":"J Big Data"},{"key":"6675_CR12","doi-asserted-by":"publisher","unstructured":"Seid MH, Woldeyohannis MM (2022) Customer churn prediction using machine learning: commercial bank of Ethiopia. In: 2022 International Conference on Information and Communication Technology for Development for Africa (ICT4DA). IEEE, Bahir Dar, Ethiopia, pp 1\u20136. https:\/\/doi.org\/10.1109\/ICT4DA56482.2022.9971224","DOI":"10.1109\/ICT4DA56482.2022.9971224"},{"key":"6675_CR13","doi-asserted-by":"publisher","unstructured":"Maan J, Maan H (2023) Customer churn prediction model using explainable machine learning https:\/\/doi.org\/10.48550\/arXiv.2303.00960","DOI":"10.48550\/arXiv.2303.00960"},{"key":"6675_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.simpat.2015.03.003","volume":"55","author":"T Vafeiadis","year":"2015","unstructured":"Vafeiadis T, Diamantaras KI, Sarigiannidis G, Chatzisavvas KC (2015) A comparison of machine learning techniques for customer churn prediction. Simul Model Pract Theory 55:1\u20139. https:\/\/doi.org\/10.1016\/j.simpat.2015.03.003","journal-title":"Simul Model Pract Theory"},{"key":"6675_CR15","doi-asserted-by":"crossref","unstructured":"Khodadadi A, Hosseini SA, Pajouheshgar E, Mansouri F, Rabiee HR (2019) ChOracle: a unified statistical framework for churn prediction. arXiv:1909.06868 [cs, stat]. http:\/\/arxiv.org\/abs\/1909.06868","DOI":"10.1109\/TKDE.2020.3000456"},{"issue":"3","key":"6675_CR16","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.32604\/csse.2022.025029","volume":"43","author":"N Almufadi","year":"2022","unstructured":"Almufadi N, Mustafa Qamar A (2022) Deep convolutional neural network based churn prediction for telecommunication industry. Comput Syst Sci Eng 43(3):1255\u20131270. https:\/\/doi.org\/10.32604\/csse.2022.025029","journal-title":"Comput Syst Sci Eng"},{"issue":"1","key":"6675_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/ijwltt.300342","volume":"17","author":"J Rabbah","year":"2022","unstructured":"Rabbah J, Ridouani M, Hassouni L (2022) A new churn prediction model based on deep insight features transformation for convolution neural network architecture and stacknet. Int J Web-Based Learn Teach Technol 17(1):1\u201318. https:\/\/doi.org\/10.4018\/ijwltt.300342","journal-title":"Int J Web-Based Learn Teach Technol"},{"issue":"2","key":"6675_CR18","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1007\/s12559-018-9608-3","volume":"11","author":"Q-F Wang","year":"2019","unstructured":"Wang Q-F, Xu M, Hussain A (2019) Large-scale ensemble model for customer churn prediction in search ads. Cogn Comput 11(2):262\u2013270. https:\/\/doi.org\/10.1007\/s12559-018-9608-3","journal-title":"Cogn Comput"},{"key":"6675_CR19","doi-asserted-by":"publisher","first-page":"854","DOI":"10.7717\/peerj-cs.854","volume":"8","author":"S Fakhar Bilal","year":"2022","unstructured":"Fakhar Bilal S, Ali Almazroi A, Bashir S, Hassan Khan F, Ali Almazroi A (2022) An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry. PeerJ Comput Sci 8:854. https:\/\/doi.org\/10.7717\/peerj-cs.854","journal-title":"PeerJ Comput Sci"},{"issue":"7","key":"6675_CR20","doi-asserted-by":"publisher","first-page":"0180735","DOI":"10.1371\/journal.pone.0180735","volume":"12","author":"S Kim","year":"2017","unstructured":"Kim S, Choi D, Lee E, Rhee W (2017) Churn prediction of mobile and online casual games using play log data. PLoS ONE 12(7):0180735. https:\/\/doi.org\/10.1371\/journal.pone.0180735","journal-title":"PLoS ONE"},{"issue":"2","key":"6675_CR21","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1109\/TG.2021.3067114","volume":"14","author":"A Perisic","year":"2022","unstructured":"Perisic A, Pahor M (2022) RFM-LIR feature framework for churn prediction in the mobile games market. IEEE Trans Games 14(2):126\u2013137. https:\/\/doi.org\/10.1109\/TG.2021.3067114","journal-title":"IEEE Trans Games"},{"issue":"6","key":"6675_CR22","doi-asserted-by":"publisher","first-page":"2795","DOI":"10.3390\/app12062795","volume":"12","author":"K Musta\u010d","year":"2022","unstructured":"Musta\u010d K, Ba\u010di\u0107 K, Skorin-Kapov L, Suz\u0306njevi\u0107 M (2022) Predicting player churn of a free-to-play mobile video game using supervised machine learning. Appl Sci 12(6):2795. https:\/\/doi.org\/10.3390\/app12062795","journal-title":"Appl Sci"},{"key":"6675_CR23","doi-asserted-by":"publisher","unstructured":"Kilimci ZH, Yoruk H, Akyokus S (2020) Sentiment analysis based churn prediction in mobile games using word embedding models and deep learning algorithms. In: 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). IEEE, Novi Sad, Serbia, pp 1\u20137. https:\/\/doi.org\/10.1109\/INISTA49547.2020.9194624","DOI":"10.1109\/INISTA49547.2020.9194624"},{"key":"6675_CR24","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1108\/JM2-01-2021-0032","volume":"17","author":"MU Tariq","year":"2022","unstructured":"Tariq MU, Babar M, Poulin M, Khattak AS (2022) Distributed model for customer churn prediction using convolutional neural network. J Model Manag 17:853\u2013863. https:\/\/doi.org\/10.1108\/JM2-01-2021-0032","journal-title":"J Model Manag"},{"key":"6675_CR25","doi-asserted-by":"publisher","first-page":"69130","DOI":"10.1109\/ACCESS.2024.3401247","volume":"12","author":"U Gani Joy","year":"2024","unstructured":"Gani Joy U, Hoque KE, Nazim Uddin M, Chowdhury L, Park S-B (2024) A big data-driven hybrid model for enhancing streaming service customer retention through churn prediction integrated with explainable AI. IEEE Access 12:69130\u201369150. https:\/\/doi.org\/10.1109\/ACCESS.2024.3401247","journal-title":"IEEE Access"},{"key":"6675_CR26","doi-asserted-by":"publisher","unstructured":"Ramesh S, Sukanth BN, Jaswanth SS, Venugopalan M (2024) A PySpark based scalable model for churn prediction using embedding models. In: 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI). IEEE, Chennai, India, pp 1\u20137. https:\/\/doi.org\/10.1109\/ACCAI61061.2024.10602333","DOI":"10.1109\/ACCAI61061.2024.10602333"},{"key":"6675_CR27","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2019.0100918","author":"Y Khan","year":"2019","unstructured":"Khan Y, Shafiq S, Naeem A, Ahmed S, Safwan N, Hussain S (2019) Customers churn prediction using artificial neural networks (ANN) in telecom industry. Int J Adv Comput Sci Appl. https:\/\/doi.org\/10.14569\/IJACSA.2019.0100918","journal-title":"Int J Adv Comput Sci Appl"},{"key":"6675_CR28","doi-asserted-by":"publisher","first-page":"60134","DOI":"10.1109\/ACCESS.2019.2914999","volume":"7","author":"I Ullah","year":"2019","unstructured":"Ullah I, Raza B, Malik AK, Imran M, Islam SU, Kim SW (2019) A churn prediction model using random forest: analysis of machine learning techniques for churn prediction and factor identification in telecom sector. IEEE Access 7:60134\u201360149. https:\/\/doi.org\/10.1109\/ACCESS.2019.2914999","journal-title":"IEEE Access"},{"key":"6675_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116277","volume":"191","author":"A Peri\u0161i\u0107","year":"2022","unstructured":"Peri\u0161i\u0107 A, Jung DS, Pahor M (2022) Churn in the mobile gaming field: establishing churn definitions and measuring classification similarities. Expert Syst Appl 191:116277. https:\/\/doi.org\/10.1016\/j.eswa.2021.116277","journal-title":"Expert Syst Appl"},{"key":"6675_CR30","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/978-3-030-59416-9_16","volume-title":"Database Systems for Advanced Applications","author":"A Zheng","year":"2020","unstructured":"Zheng A, Chen L, Xie F, Tao J, Fan C, Zheng Z (2020) Keep you from leaving: churn prediction in online games. In: Nah Y, Cui B, Lee S-W, Yu JX, Moon Y-S, Whang SE (eds) Database Systems for Advanced Applications, vol 12113. Springer, Cham, pp 263\u2013279. https:\/\/doi.org\/10.1007\/978-3-030-59416-9_16"},{"issue":"5","key":"6675_CR31","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.3390\/math11051137","volume":"11","author":"M Bogaert","year":"2023","unstructured":"Bogaert M, Delaere L (2023) Ensemble methods in customer churn prediction: a comparative analysis of the state-of-the-art. Mathematics 11(5):1137. https:\/\/doi.org\/10.3390\/math11051137","journal-title":"Mathematics"},{"key":"6675_CR32","doi-asserted-by":"publisher","first-page":"68017","DOI":"10.1109\/ACCESS.2022.3185227","volume":"10","author":"S De","year":"2022","unstructured":"De S, Prabu P (2022) A sampling-based stack framework for imbalanced learning in churn prediction. IEEE Access 10:68017\u201368028. https:\/\/doi.org\/10.1109\/ACCESS.2022.3185227","journal-title":"IEEE Access"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06675-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06675-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06675-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T18:06:53Z","timestamp":1731607613000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06675-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6675"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06675-9","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,14]]},"assertion":[{"value":"31 October 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Ethical approval is not applicable for this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Written informed consent for publication was obtained from all participants.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"178"}}