Infusing Geo-Recency Mixture Models for Effective Location Prediction in LBSN
Roland Assam, Subramanyam Sathyanarayana, Thomas Seidl · 2016
An overwhelming volume of geo-social data is generated daily. This data could be analyzed and used to predict or expose valuable vivid patterns that would be imperative to society. However, prediction on sparse check-in data in Location-Based Social Networks (LBSN) is still challenging. In this paper, we propose a novel technique that utilizes matrix factorization to predict the top-k future locations for check-in data. Specifically, we introduce a new approach to capture the unique mobility behaviors of users by crafting Geo-Recency based Gaussian mixture models. Then, we determine users or friends with similar check-in preferences by computing the Cross Likelihood Ratio (CLR) similarity from each user's Geo-Recency Gaussian model. These CLR scores are leveraged during matrix factorization to learn and predict future locations. In addition, we present a new technique that employs these Geo-Recency Gaussian mixture models to effectively quantify social influence. We conduct numerous experiments on real datasets and compare our empirical results to that of state-of-the-art works. Our experiments reveal that our future location prediction technique performs better than that of state-of-the-art works.