Title: A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games,
Abstract: 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.