Point-of-interest recommendation in location-based social networks with personalized geo-social influence

Point-of-interest (POI) recommendation is a popular topic on location-based social networks (LBSNs). Geographical proximity, known as a unique feature of LBSNs, significantly affects user check-in behavior. However, most of prior studies characterize the geographical influence based on a universal or personalized distribution of geographic distance, leading to unsatisfactory recommendation results. In this paper, the personalized geographical influence in a two-dimensional geographical space is modeled using the data field method, and we propose a semi-supervised probabilistic model based on a factor graph model to integrate different factors such as the geographical influence. Moreover, a distributed learning algorithm is used to scale up our method to large-scale data sets. Experimental results based on the data sets from Foursquare and Gowalla show that our method outperforms other competing POI recommendation techniques.