app Popularity Prediction with Multi-Level Attention Networks

The popularity prediction of mobile apps provides substantial value to a broad range of applications, ranging from operational strategy optimization to targeted advertising and investment. This work includes leveraging the rich data provided by the app market to mine the dynamic correlation between different factors and popularity, so as to predict the app popularity over the next period of time, which creates great value for developers, investors and the app market. However, the evolution of app popularity is highly dynamic, and its influence factors are very complex, including the iterative evolution of the app itself, user feedback, and competition for similar products and so on. At present, there are relatively few research studies on app popularity modeling and prediction. Most of them construct artificial features and capture its association with popularity, and there is room for improvement in terms of computational performance, prediction accuracy, and interpretability of results. In this paper, we propose DeePOP, an attention based neural network for app popularity modeling and prediction, which performs hierarchical modeling for complex influence factors. First, we propose the time-level self-sequence module to capture the long-term dependence on historical popularity, and propose the local and global feature level modules to capture the nonlinear relationship between features and app popularity. Second, the attention mechanisms provide adaptive capabilities for different modules to capture most relevant historical states and provide explanation for prediction. Last, the experimental results show that DeePOP outperforms the state-of-the-art methods and the root mean square error of prediction reaches up to 0.089.