A Synthetic Recommendation Model for Point-of-Interest on Location-Based Social Networks: Exploiting Contextual Information and Review

With the rapid growth of location-based social network (LBSN), point-of-interest (POI) recommendation has become an important mean to help people discover attractive locations. However, most of existing models of POI recommendation on LBSNs improve recommendation quality by exploiting the user check-in history behavior and contextual information(e.g., geographical information and social correlations), and they tend to ignore the review texts information accompanied with rating information for recommender models. While in reality, users only check in a few POIs in LBSN, which makes the user-POIs check-in history records and contextual information highly sparse, and causes a big challenge for POIs recommendations. To tackle this challenge, a novel POIs recommendation model called GeoSoRev is proposed in this paper, which combines users’ preference to a POI with geographical information, social correlations and reviews text on the basis of the classic recommendation model based on matrix factorization. Experimental results on two real-world datasets collected from Foursquare show that GeoSoRev achieves significantly superior precision and recalling rates compared with other state-of-the-art POIs recommendation models.