Predictor: an app cold start recommender system based on user similarity and app periodicity

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). However, they all fail to take into account App usage periodicity. Therefore, we designed Predictor, a RS 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 recommendations, and matches the launching expectations of most users.

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