A Probabilistic Top-N algorithm for mobile applications recommendation

Being one of the most essential techniques in recommender systems, current researches on Collaborative Filtering focus on how to improve the accuracy. However, it is of the same significance to recommend more potential interests to users because of their expectations for recommendation list besides the accuracy. Since current recommender systems rarely address this problem, we focus on how to help users find more interests in the recommendation list. A sampling-based algorithm, name-ly, Probabilistic Top-N Selection, is proposed to recommend potential interests for users. A series of experiments are conducted against a mobile application dataset obtained from an application market in China. The experimental results demonstrate that our algorithm can improve the recommendation effectively.