Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …

Recommendation systems are gaining increasing popularity in many application areas like
e-commerce, movie and music recommendations, tourism, news, advertisement, stock …

Location Based Social Networks (LBSNs) have been widely used as a primary data source
to study the impact of mobility and social relationships on each other. Traditional …

Location prediction is a key problem in human mobility modeling, which predicts a user's
next location based on historical user mobility traces. As a sequential prediction problem by …

Point-of-Interest (POI) recommendation has become an important means to help people
discover attractive locations. However, extreme sparsity of user-POI matrices creates a …

With the recent surge of location based social networks (LBSNs), activity data of millions of
users has become attainable. This data contains not only spatial and temporal stamps of …

Context-aware recommender systems leverage the value of recommendations by exploiting
context information that affects user preferences and situations, with the goal of …

Locations, eg, countries, states, cities, and point-of-interests, are central to news, emergency
events, and people's daily lives. Automatic identification of locations associated with or …

Location-based social networks (LBSNs) have attracted an inordinate number of users and
greatly enriched the urban experience in recent years. The availability of spatial, temporal …

The rapid growth of Location-based Social Networks (LBSNs) provides a vast amount of
check-in data, which facilitates the study of point-of-interest (POI) recommendation. The …