AppTrends: A graph-based mobile app recommendation system using usage history

Donghwan Bae, Keejun Han, Juneyoung Park, Mun Yong Yi

Abstract: With the advent of smartphones, mobile phones have evolved from a simple communication tool to a multipurpose device that affects every aspect of our daily life. The expansion of the mobile application market has made it difficult for smartphone users to find applications that fit their needs. Most prior research on application recommendation provides a limited solution to the problem of application overload. These recommendation techniques, developed outside of the mobile environment, have a number of limitations such as cold start problem and domain disparity. In this paper, we propose AppTrends, which incorporates a graph-based technique for application recommendation in the Android OS environment. Our experiment results obtained from the field usage record of over 4 million applications clearly show that the proposed graph-based recommendation model is more accurate than the Slope One Model.