Mining Mobile App Data for Churn Prediction and Personalized
发布时间:2018-06-19 浏览:225

报告题目:Mining Mobile App Data for Churn Prediction and Personalized Recommendation



报告人:葛永(Yong Ge) 教授



报告摘要:Mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. One critical challenge for these platforms and services is to understand user churn behavior in mobile games. Successful churn prediction will benefit many stakeholders such as game developers and platform operators. In this talk, I will first present a large-scale churn prediction solution for mobile games. I will demonstrate the performance of our solution by using the collected real-world data from a commercial mobile gaming platform that includes tens of thousands of games and hundreds of millions of user-app interactions. Also, I will briefly present a framework for mobile game app recommendation that includes both offline and online components.


报告人简介:Dr. Yong Ge is an assistant professor at MIS Dept. of University of Arizona (UoA). He received his Ph.D. in Information Technology from Rutgers, The State University of New Jersey in 2013, the M.S. degree in Signal and Information Processing from the University of Science and Technology of China (USTC) in 2008, and the B.E. degree in Information Engineering from Xi'an Jiao Tong University in 2005. He received the ICDM-2011 Best Research Paper Award, Excellence in Academic Research (one per school) at Rutgers Business School in 2013, and the Dissertation Fellowship at Rutgers University in 2012. He has published prolifically in refereed journals and conference proceedings, such as IEEE TKDE, ACM TOIS, ACM TKDD, ACM TIST, ACM SIGKDD, and IEEE ICDM. His works have been supported by UoA, NSF and NIH.