Exploring Social Influence on Location-Based Social Networks

Abstract

Recently, with the advent of location-based social networking services (LBSNs), travel planning and location-aware information recommendation based on LBSNs have attracted much research attention. In this paper, we study the impact of social relations hidden in LBSNs, i.e., the social influence of friends. We propose a new social influence-based user recommender framework (SIR) to discover the potential value from reliable users (i.e., close friends and travel experts). Explicitly, our SIR framework is able to infer influential users from an LBSN. We claim to capture the interactions among virtual communities, physical mobility activities and time effects to infer the social influence between user pairs. Furthermore, we intend to model the propagation of influence using diffusion-based mechanism. Moreover, we have designed a dynamic fusion framework to integrate the features mined into a united follow probability score. Finally, our SIR framework provides personalized top-k user recommendations for individuals. To evaluate the recommendation results, we have conducted extensive experiments on real datasets (i.e., the Gowalla dataset). The experimental results show that the performance of our SIR framework is better than the state-of-the-art user recommendation mechanisms in terms of accuracy and reliability.