{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T06:08:31Z","timestamp":1764050911052,"version":"3.45.0"},"reference-count":35,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,9]]},"DOI":"10.1109\/dsaa65442.2025.11248010","type":"proceedings-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T18:56:45Z","timestamp":1764010605000},"page":"1-10","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Player Churn with LLMs: A Comprehensive Evaluation of World Knowledge and Reasoning"],"prefix":"10.1109","author":[{"given":"Tobias","family":"Schneider","sequence":"first","affiliation":[{"name":"Fraunhofer IAIS,Sankt Augustin,Germany"}]},{"given":"Lorenz","family":"Sparrenberg","sequence":"additional","affiliation":[{"name":"University of Bonn,Bonn,Germany"}]},{"given":"Rafet","family":"Sifa","sequence":"additional","affiliation":[{"name":"Fraunhofer IAIS,Sankt Augustin,Germany"}]}],"member":"263","reference":[{"journal-title":"On the opportunities and risks of foundation models","year":"2022","author":"Bommasani","key":"ref1"},{"volume-title":"Introducing GPT-4.1 in the API","year":"2025","key":"ref2"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CIG.2014.6932876"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3390\/fi16120453"},{"volume-title":"DDraceNetwork website","year":"2025","key":"ref5"},{"volume-title":"Contrastive preference optimization: Pushing the boundaries of llm performance in machine translation","year":"2024","author":"Xu","key":"ref6"},{"key":"ref7","article-title":"ToolQA: A dataset for LLM question answering with external tools","volume-title":"Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track","author":"Zhuang","year":"2023"},{"key":"ref8","first-page":"5549","article-title":"Tabllm: Few-shot classification of tabular data with large language models","volume-title":"Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, ser. Proceedings of Machine Learning Research","volume":"206","author":"Hegselmann","year":"2023"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/BigData59044.2023.10386518"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/BigData59044.2023.10386911"},{"volume-title":"Language models are few-shot learners","year":"2020","author":"Brown","key":"ref11"},{"volume-title":"Measuring massive multitask language understanding","year":"2021","author":"Hendrycks","key":"ref12"},{"volume-title":"On the unexpected abilities of large language models","year":"2023","author":"Nolfi","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1201\/9780429286490-5"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1609\/aiide.v11i1.12788"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3167918.3167925"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-023-00721-8"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CoG52621.2021.9619059"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CIG.2014.6932875"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CIG.2019.8848106"},{"issue":"1","key":"ref21","first-page":"1414","volume-title":"Customer churn prediction in telecommunications","volume":"39","author":"Huang","year":"2012"},{"key":"ref22","first-page":"34","article-title":"Churn prediction","volume":"33","author":"Lazarov","year":"2007","journal-title":"Bus. Anal. Course. TUM Comput. Sci"},{"issue":"1","key":"ref23","doi-asserted-by":"crossref","first-page":"165","DOI":"10.3390\/jtaer17010009","article-title":"Customer churn in retail e-commerce business: Spatial and machine learning approach","volume":"17","author":"Matuszelanski","year":"2022","journal-title":"Journal of Theoretical and Applied Electronic Commerce Research"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"116277","DOI":"10.1016\/j.eswa.2021.116277","article-title":"Churn in the mobile gaming field: Establishing churn definitions and measuring classification similarities","volume":"191","author":"Perisic","year":"2022","journal-title":"Expert Systems with Applications"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/cig.2016.7860405"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-52249-0_21"},{"issue":"5","key":"ref27","doi-asserted-by":"crossref","DOI":"10.3390\/info13050227","article-title":"An approach to churn prediction for cloud services recommendation and user retention","volume":"13","author":"Saias","year":"2022","journal-title":"Information"},{"volume-title":"Teehistorian documentation","year":"2025","key":"ref28"},{"key":"ref29","article-title":"Large language models are zero-shot reasoners","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems, ser. NIPS \u201922. Red Hook","author":"Kojima","year":"2022"},{"journal-title":"Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models","year":"2023","author":"Wang","key":"ref30"},{"volume-title":"Take a step back: Evoking reasoning via abstraction in large language models","year":"2024","author":"Zheng","key":"ref31"},{"key":"ref32","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2024.emnlp-main.222","volume-title":"Conditional and modal reasoning in large language models","author":"Holliday","year":"2024"},{"key":"ref33","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2023.acl-long.244","volume-title":"On second thought, let\u2019s not think step by step! bias and toxicity in zero-shot reasoning","author":"Shaikh","year":"2023"},{"volume-title":"Chain-of-thought prompting elicits reasoning in large language models","year":"2023","author":"Wei","key":"ref34"},{"volume-title":"Automatic chain of thought prompting in large language models","year":"2022","author":"Zhang","key":"ref35"}],"event":{"name":"2025 IEEE 12th International Conference on Data Science and Advanced Analytics (DSAA)","start":{"date-parts":[[2025,10,9]]},"location":"Birmingham, United Kingdom","end":{"date-parts":[[2025,10,12]]}},"container-title":["2025 IEEE 12th International Conference on Data Science and Advanced Analytics (DSAA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11247920\/11247921\/11248010.pdf?arnumber=11248010","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T05:58:59Z","timestamp":1764050339000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11248010\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,9]]},"references-count":35,"URL":"https:\/\/doi.org\/10.1109\/dsaa65442.2025.11248010","relation":{},"subject":[],"published":{"date-parts":[[2025,10,9]]}}}