解读显性和隐性特征,为在线视频游戏提供可靠、可解释和可操作的用户流失预测。

Xiyuan Wang, Laixin Xie, He Wang, Xingxing Xing, Wei Wan, Ziming Wu, Xiaojuan Ma, Quan Li
{"title":"解读显性和隐性特征,为在线视频游戏提供可靠、可解释和可操作的用户流失预测。","authors":"Xiyuan Wang, Laixin Xie, He Wang, Xingxing Xing, Wei Wan, Ziming Wu, Xiaojuan Ma, Quan Li","doi":"10.1109/TVCG.2024.3487974","DOIUrl":null,"url":null,"abstract":"<p><p>The burgeoning online video game industry has sparked intense competition among providers to both expand their user base and retain existing players, particularly within social interaction genres. To anticipate player churn, there is an increasing reliance on machine learning (ML) models that focus on social interaction dynamics. However, the prevalent opacity of most ML algorithms poses a significant hurdle to their acceptance among domain experts, who often view them as \"black boxes\". Despite the availability of eXplainable Artificial Intelligence (XAI) techniques capable of elucidating model decisions, their adoption in the gaming industry remains limited. This is primarily because non-technical domain experts, such as product managers and game designers, encounter substantial challenges in deciphering the \"explicit\" and \"implicit\" features embedded within computational models. This study proposes a reliable, interpretable, and actionable solution for predicting player churn by restructuring model inputs into explicit and implicit features. It explores how establishing a connection between explicit and implicit features can assist experts in understanding the underlying implicit features. Moreover, it emphasizes the necessity for XAI techniques that not only offer implementable interventions but also pinpoint the most crucial features for those interventions. Two case studies, including expert feedback and a within-subject user study, demonstrate the efficacy of our approach.</p>","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deciphering Explicit and Implicit Features for Reliable, Interpretable, and Actionable User Churn Prediction in Online Video Games.\",\"authors\":\"Xiyuan Wang, Laixin Xie, He Wang, Xingxing Xing, Wei Wan, Ziming Wu, Xiaojuan Ma, Quan Li\",\"doi\":\"10.1109/TVCG.2024.3487974\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The burgeoning online video game industry has sparked intense competition among providers to both expand their user base and retain existing players, particularly within social interaction genres. To anticipate player churn, there is an increasing reliance on machine learning (ML) models that focus on social interaction dynamics. However, the prevalent opacity of most ML algorithms poses a significant hurdle to their acceptance among domain experts, who often view them as \\\"black boxes\\\". Despite the availability of eXplainable Artificial Intelligence (XAI) techniques capable of elucidating model decisions, their adoption in the gaming industry remains limited. This is primarily because non-technical domain experts, such as product managers and game designers, encounter substantial challenges in deciphering the \\\"explicit\\\" and \\\"implicit\\\" features embedded within computational models. This study proposes a reliable, interpretable, and actionable solution for predicting player churn by restructuring model inputs into explicit and implicit features. It explores how establishing a connection between explicit and implicit features can assist experts in understanding the underlying implicit features. Moreover, it emphasizes the necessity for XAI techniques that not only offer implementable interventions but also pinpoint the most crucial features for those interventions. Two case studies, including expert feedback and a within-subject user study, demonstrate the efficacy of our approach.</p>\",\"PeriodicalId\":94035,\"journal\":{\"name\":\"IEEE transactions on visualization and computer graphics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on visualization and computer graphics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TVCG.2024.3487974\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on visualization and computer graphics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TVCG.2024.3487974","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

蓬勃发展的在线视频游戏行业引发了供应商之间的激烈竞争,他们既要扩大用户群,又要留住现有玩家,尤其是社交互动类型的游戏。为了预测玩家流失率,人们越来越依赖于关注社交互动动态的机器学习(ML)模型。然而,大多数 ML 算法普遍不透明,这严重阻碍了该领域专家对它们的接受,他们通常将这些算法视为 "黑盒子"。尽管可解释人工智能(XAI)技术能够阐明模型决策,但其在游戏行业的应用仍然有限。这主要是因为非技术领域专家(如产品经理和游戏设计师)在解读蕴含在计算模型中的 "显性 "和 "隐性 "特征时遇到了巨大挑战。本研究通过将模型输入重组为显性和隐性特征,为预测玩家流失率提出了一种可靠、可解释和可操作的解决方案。它探讨了在显性特征和隐性特征之间建立联系如何有助于专家理解潜在的隐性特征。此外,它还强调了 XAI 技术的必要性,这些技术不仅能提供可实施的干预措施,还能为这些干预措施指出最关键的特征。包括专家反馈和主体内用户研究在内的两个案例研究证明了我们方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Deciphering Explicit and Implicit Features for Reliable, Interpretable, and Actionable User Churn Prediction in Online Video Games.

The burgeoning online video game industry has sparked intense competition among providers to both expand their user base and retain existing players, particularly within social interaction genres. To anticipate player churn, there is an increasing reliance on machine learning (ML) models that focus on social interaction dynamics. However, the prevalent opacity of most ML algorithms poses a significant hurdle to their acceptance among domain experts, who often view them as "black boxes". Despite the availability of eXplainable Artificial Intelligence (XAI) techniques capable of elucidating model decisions, their adoption in the gaming industry remains limited. This is primarily because non-technical domain experts, such as product managers and game designers, encounter substantial challenges in deciphering the "explicit" and "implicit" features embedded within computational models. This study proposes a reliable, interpretable, and actionable solution for predicting player churn by restructuring model inputs into explicit and implicit features. It explores how establishing a connection between explicit and implicit features can assist experts in understanding the underlying implicit features. Moreover, it emphasizes the necessity for XAI techniques that not only offer implementable interventions but also pinpoint the most crucial features for those interventions. Two case studies, including expert feedback and a within-subject user study, demonstrate the efficacy of our approach.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
"where Did My Apps Go?" Supporting Scalable and Transition-Aware Access to Everyday Applications in Head-Worn Augmented Reality. PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction. From Dashboard Zoo to Census: A Case Study With Tableau Public. Authoring Data-Driven Chart Animations. Super-NeRF: View-consistent Detail Generation for NeRF Super-resolution.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1