POI Recommendation: A Temporal Matching between POI Popularity and User Regularity
Point of interest (POI) recommendation, which provides personalized recommendation of places to mobile users, is an important task in location based social networks (LBSNs) However, quite different from 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 included the temporal effect into POI recommendations, they overlooked the compatibility between time varying popularity of POIs and regular availability of users, which we believe has a non negligible impact on user decision making. To this end, in this paper, we present a novel method which incorporates the degree of temporal matching between users and POIs into personalized POI recommendations. Specifically, we first profile the temporal popularity of POIs to show when a POI is popular for visit by mining the spatio temporal human mobility and POI category data. Secondly, we propose latent user regularities to characterize when a user is regularly available for exploring POIs, which is learned with a user POI temporal matching function. Finally, results of extensive experiments with real world POI check in and human mobility data demonstrate that our proposed user POI temporal matching method delivers substantial advantages over baseline models for POI recommendation tasks.
2016 IEEE 16th International Conference on Data Mining (ICDM)