Location-based and preference-aware recommendation using sparse geo-social networking data

The popularity of location-based social networks provide us with a new platform to understand users ’ preferences based on their lo-cation histories. In this paper, we present a location-based and preference-aware recommender system that offers a particular user a set of venues (such as restaurants) within a geospatial range with the consideration of both: 1) User preferences, which are auto-matically learned from her location history and 2) Social opin-ions, which are mined from the location histories of the local ex-perts. This recommender system can facilitate people’s travel not only near their living areas but also to a city that is new to them. As a user can only visit a limited number of locations, the user-locations matrix is very sparse, leading to a big challenge to tradi-tional collaborative filtering-based location recommender systems. The problem becomes even more challenging when people travel t