A network traffic-aware mobile application recommendation system based on network traffic cost consideration

Abstract

A large amount and different types of mobile applications (or apps) are being offered to end users via app markets. Existing mobile app recommender systems generally recommend the most popular mobile apps to mobile users to facilitate the proper selection of mobile apps. However, these apps normally generate network traffic, which will consume the user's mobile data plan and may even cause potential security issues. Therefore, more and more mobile users are hesitant or even reluctant to use the mobile apps that are recommended by the mobile app markets. To fill this crucial gap, this paper proposes a mobile app recommendation approach which can provide app recommendations by considering both the apps' popularity and their traffic cost. To achieve this goal, this paper first estimates app network traffic score based on bipartite graph. Then, this paper proposes a flexible approach based on benefit-cost analysis, which can recommend apps by maintaining a balance between the apps' popularity and the traffic cost concern. Finally, this paper evaluates our approach with extensive experiments on a large-scale data set collected from Google Play. The experimental results clearly validate the effectiveness and efficiency of our approach.