A recommender system to address the Cold Start problem for App usage prediction

The Cold Start Recommender System (RS) for App usage prediction on mobile phones is important for improving new user experience on mobile operating systems. At present, the existing Cold Start RS computes the probability of App launching mainly by mining the potential information of new users and similar users (i.e., collaborative filtering algorithm CF). But for newly installed Apps, the default CF does not have any useful information for App Cold Start recommendation, resulting in the data sparseness. To tackle the problems, we think that App usage periodicity installed by the new user is regularly followed under different conditions. It not only compensates for the data sparseness of similar users but also increases the predictive flexibility when the user’s environment changes. Therefore, we designed Predictor, an efficient dynamic CF fusion algorithm that provides App Cold Start prediction for new users on mobile devices. It dynamically combines both App preferences of similar users (user-based CF) and App usage periodicity (item-based CF) through the conditional combination. Compared to other traditional methods, Predictor proposes more appropriate App launching recommendation and matches the launching expectations of most users.