TY - GEN
T1 - A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games
AU - Liu, Xi
AU - Xie, Muhe
AU - Wen, Xidao
AU - Chen, Rui
AU - Ge, Yong
AU - Duffield, Nick
AU - Wang, Na
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/27
Y1 - 2018/12/27
N2 - 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. Accurate churn prediction will benefit many stakeholders such as game developers, advertisers, and platform operators. In this paper, we present the first large-scale churn prediction solution for mobile games. In view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semi-supervised and inductive embedding model that jointly learns the prediction function and the embedding function for user-app relationships. We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics. We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities. To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions. The experimental results with this data demonstrate the superiority of our proposed model against existing state-of-the-art methods.
AB - 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. Accurate churn prediction will benefit many stakeholders such as game developers, advertisers, and platform operators. In this paper, we present the first large-scale churn prediction solution for mobile games. In view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semi-supervised and inductive embedding model that jointly learns the prediction function and the embedding function for user-app relationships. We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics. We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities. To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions. The experimental results with this data demonstrate the superiority of our proposed model against existing state-of-the-art methods.
KW - Churn prediction
KW - Graph embedding
KW - Mobile apps
KW - Representation learning
KW - Semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85061376191&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061376191&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2018.00043
DO - 10.1109/ICDM.2018.00043
M3 - Conference contribution
AN - SCOPUS:85061376191
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 277
EP - 286
BT - 2018 IEEE International Conference on Data Mining, ICDM 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Conference on Data Mining, ICDM 2018
Y2 - 17 November 2018 through 20 November 2018
ER -