Exploiting Location Significance and User Authority for Point-of-Interest Recommendation

With the rapid growth of location-based social networks (LBSNs), point-of-interest (POI) recommendation has become indispensable. Several approaches have been proposed to support personalized POI recommendation in LBSNs. However, most of the existing matrix factorization based methods treat users’ check-in frequencies as ratings in traditional recommender systems and model users’ check-in behaviors using the Gaussian distribution, which is unsuitable for modeling the heavily skewed frequency data. In addition, little methods systematically consider the effects of location significance and user authority on users’ final check-in decision processes. In this paper, we integrate probabilistic factor model and location significance to model users’ check-in behaviors, and propose a location significance and user authority enhanced probabilistic factor model. Specifically, a hybrid model of HITS and PageRank is adapted to compute user authority and location significance. Moreover, user authorities are used to weight users’ implicit feedback. Experimental results on two real world data sets show that our proposed approach outperforms the state-of-the-art POI recommendation algorithms.