The wide spread of location-based social networks brings about a huge volume of user
check-in data, which facilitates the recommendation of points of interest (POIs). Recent …

The wide spread of location-based social networks brings about a huge volume of user
check-in data, which facilitates the recommendation of points of interest (POIs). Recent …

Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare,
Brightkite, and so on, have attracted millions of users to share their social friendship and …

Point-of-interest (POI) recommendation that suggests new places for users to visit arises with
the popularity of location-based social networks (LBSNs). Due to the importance of POI …

The emergence of Location-based Social Network (LBSN) services allows users to share
their check-ins, providing an excellent opportunity to build personalized Point-of-Interest …

As one of the important techniques to explore unknown places for users, the methods that
are proposed for point‐of‐interest (POI) recommendation have been widely studied in recent …

The increasing proliferation of location-based social networks brings about a huge volume
of user check-in data, which facilitates the recommendation of points of interest (POIs). Time …

The huge amount of heterogeneous information in location-based social networks (LBSNs)
creates great challenges for POI recommendation. User check-in behavior exhibits two …

The check-in behaviors of users are ubiquitous in location-based social networks in urban
living. Understanding user preferences is critical to improving the recommendation services …

A large volume of data flowing throughout location-based social networks (LBSN) gives
support to the recommendation of points-of-interest (POI). One of the major challenges that …