Most existing studies on next location recommendation propose to model the sequential
regularity of check-in sequences, but suffer from the severe data sparsity issue where most …

The next location recommendation is at the core of various location-based applications.
Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical …

Sequential location recommendation plays an important role in many applications such as
mobility prediction, route planning and location-based advertisements. In spite of evolving …

Next Point-of Interest (POI) recommendation plays an important role in location-based
applications, which aims to recommend the next POIs to users that they are most likely to …

Next Point-of-Interest (POI) recommendation is of great value for both location-based service
providers and users. Recently Recurrent Neural Networks (RNNs) have been proved to be …

Conventional location recommendation models rely on users' visit history, geographical
influence, temporal influence, etc., to infer users' preferences for locations. However …

Location recommendation is an important means to help people discover attractive
locations. However, extreme sparsity of user-location matrices leads to a severe challenge …

With the growing popularity of location-based social networks, numerous location visiting
records (eg, check-ins) continue to accumulate over time. The more these records are …

With the prosperity of the location-based social networks, next point-of-interest (POI)
recommendation has become an increasingly significant requirement since it can benefit …

Recommending yet-unvisited points of interest (POIs) which may be of interest to users is
one of the fundamental applications in location-based social networks. It mainly replies on …