An analysis on the impact of geolocation in recommending venues in location-based social networks

The pervasiveness of geo-located devices has opened new possibilities in recommender systems on social networks. In effect, Location-Based Social Networks or LBSNs are a relatively new breed of social networks that let users share their location by triggering ”check-in” events on venues, such as businesses or historical places. In this paper, we compare the performance of traditional rating and social-based similarity metrics against location-based metrics in a userbased collaborative filtering algorithm that recommends venues or places to visit. This analysis was performed on a large real-world dataset provided by the Yelp social network service. Our results show that, geo-located metrics perform as well as rating or social metrics for selecting like-minded users and, thus, to issue a recommendation.