AppLife framework improves app survival prediction using MTL


App survival prediction is a significant task in mobile service development. It differs from existing prediction tasks in two aspects. First, rather than the traditional survival prediction in bioinformatics where all the patients’ survival probabilities decay in a similar way, apps’ survival pattern varies from each other. Second, affected by multiple factors, an app’s popularity is time-varying and sequence-dependent, which makes existing short-term prediction methods not applicable due to error accumulation. These characteristics bring great difficulties in app survival prediction. In this paper, we propose AppLife, a framework that fuses multi-source influence factors and utilizes Multi-Task Learning (MTL) to combine the state information of mobile app for survival prediction. First, we analyze how the app survival is affected by multi-source factors, including download history, ratings, and reviews. Second, to overcome error accumulation in long-term prediction, we propose a novel MTL based approach. The approach estimates whether an app is surviving at each time interval during the life cycle of apps and leverages relatedness among tasks to improve the prediction performance. Last, we collect a large-scale dataset with more than 35,000 apps, based on which we evaluate our proposed framework and results show that it outperforms the seven state-of-the-art methods.

IEEE Transactions on Mobile Computing