Personalized point-of-interest (POI) recommendation is a significant task in location-based
social networks (LBSNs) as it can help provide better user experience as well as enable …

The availability of user check-in data in large volume from the rapid growing location based
social networks (LBSNs) enables many important location-aware services to users. Point-of …

This paper studies the problem of recommending new venues to users who participate in
location-based social networks (LBSNs). As an increasingly larger number of users partake …

With the rapidly growing location-based social networks (LBSNs), personalized geo-social
recommendation becomes an important feature for LBSNs. Personalized geo-social …

Collaborative filtering (CF) is an important and popular technology for recommender
systems. However, current CF methods suffer from such problems as data sparsity …

This paper proposes a method that analyzes the location log data of multiple users to
recommend locations to be visited. The method uses our new topic model, called Geo Topic …

Social recommender systems leverage collaborative filtering (CF) to serve users with
content that is of potential interesting to active users. A wide spectrum of CF schemes has …

Mobile recommender systems target on recommending the right product or information to
the right mobile users at anytime and anywhere. It is well known that the contextual …

Recent advances in position localization techniques have fundamentally enhanced social
networking services, allowing users to share their locations and location-related content …

Collaborative Filtering-based recommendation algorithms have achieved widespread
success on the Web, but little work has been performed to investigate appropriate user-item …