Learning Users' Profiles in Location-based Social Networks

In recent years we have witnessed a flourish of location-based social media, such as Foursquare, Gowalla, Facebook Place, etc, which are collectively termed as location-based social networks (LBSNs). These services offer more location-tagged information to people, helping them to make better informed decisions on where to eat, sleep, shop and relax within the local context. The boom of LBSNs opens up a vast range of possibilities to study location-oriented human interactions and collective behaviours in an unprecedented scale. In this book, a community learning framework in LBSNs is introduced. It first detects and understands user communities based on the heterogeneous interactions in LBSNs. It then studies community matching across geographical regions in the context of generating personalized recommendations of locally interesting venues to tourists. A large-scale, representative and real world dataset is sampled from Foursquare and extensive experiments are conducted to verify the effectiveness of the proposed approaches.

Dr. Zhao Yiliang earned a Ph.D. in Computer Science from National University of Singapore (NUS). He is currently an assistant professor in Digipen Institute of Technology, Singapore. His research interests include social network analysis, machine learning and recommendation techniques.