Point-of-interest recommendations in location-based social networks

Abstract

Location-based social networks (LBSNs), e.g., Foursquare, Gowalla and Yelp, bridge the physical world with the virtual online world. LBSNs have accumulated plenty of community-contributed data such as social links between users, check-ins of users on points-of-interest (POIs), geographical information and categories of POIs, which reflect the preferences of users to POIs. Recommending users with their preferred POIs benefits people to explore new places and businesses to discover potential customers. This paper aims to recommend personalized POIs for users based on their preferences that are learned from the community-contributed data. To this end, this paper models the social, categorical, geographical, sequential, and temporal influences on the visiting preferences of users to POIs.