Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation

Abstract

The rapid development of location-based social networks (LBSNs) has provided an unprecedented opportunity for better location-based services through Point-of-Interest (POI) recommendation. POI recommendation is personalized, location-aware, and context depended. However, extreme sparsity of user-POI matrix creates a severe challenge. In this paper, we propose a context-aware probabilistic matrix factorization method for POI recommendation. Our model is called TGSC-PMF, it exploits textual information, geographical information, social information, categorical information and popularity information, and incorporates these factors effectively. First, we exploit an aggregated Latent Dirichlet Allocation (LDA) model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score. Second, we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and then generate geographical relevance score. Third, we build social relevance through the power-law distribution of user social relations to generate social relevance score. Then, we model the categorical correlations which combine the category bias of users and the popularity of POIs into categorical relevance score. Further, we integrate the interest, geographical, social and categorical relevance scores into probabilistic matrix factorization model (PMF) for POI recommendation. Finally, we implement experiments on a real LBSN check-in dataset. Experimental results show that TGSC-PMF achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.