Exploiting User Mobility for Time-aware POI Recommendation in Social Networks

Abstract

Location-Based Social Networks (LBSNs) have recently gained increasing popularity. Timeaware Point-of-Interest(POI) recommendation is one of the most important location-aware services, which can recommend locations to a target user at the specific time based on check-in history. However, current techniques ignore user’s mobility within a region, which plays a vital role in the POI chosen decision. Most of existing algorithms fail to capture the latent relations behind the temporal factors and geographical influence. In this paper, we propose a novel hybrid Time-aware POI Recommendation model based on User Mobility: TPR-UM, which improves the POI recommendation accuracy greatly. More specifically, by introducing the implicit region factor, we capture users’ mobility through collective user actions and geographical properties of locations. We generate the check-in patterns based on different time intervals and different regions to exploit the latent relations between temporal factors and geographical influence. Finally, we conduct comprehensive experiments on two real-world datasets, Gowalla and Foursquare. The experimental results demonstrate that our hybrid method is effective and outperforms other state-of-the-art algorithms in terms of precision, recall and Fβ measurement.