Exploiting Human Mobility Patterns for Point-of-Interest Recommendation

Point-of-interest (POI) recommendation, which provides personalized recommendation of places to mobile users, is an important task in location-based social networks (LBSNs). Unlike traditional interest-oriented merchandise recommendation, POI recommendation is more complex due to the timing effects: we need to examine whether the POI fits a user's availability. While there are some prior studies which consider temporal effects by solely using check-in timestamps for modeling, they suffer from check-in data sparsity. Recent years, the advent in positioning technology has accumulated a variety of urban data related to human mobility. There is a potential to exploit human mobility patterns from heterogeneous information sources for improving POI recommendation. To this end, we propose a novel method which incorporates the degree of temporal matching between users and POIs into personalized POI recommendations. Specifically, we profile the temporal popularity of POIs, learn the latent regularity to characterize users, and conduct comprehensive experiments with real-world data. Evaluation results demonstrate the effectiveness of the proposed method.