Personalized location recommendation by aggregating multiple recommenders in diversity


Location recommendation is an important feature of social network applications and location-based services. Most existing studies focus on developing one single method or model for all users. By analyzing real location-based social networks, in this paper we reveal that the decisions of users on place visits depend on multiple factors, and different users may be affected differently by these factors. We design a location recommendation framework that combines results from various recommenders that consider various factors. Our framework estimates, for each individual user, the underlying influence of each factor to her. Based on the estimation, we aggregate suggestions from different recommenders to derive personalized recommendations. Experiments on Foursquare and Gowalla show that our proposed method outperforms the state-of-the-art methods on location recommendation.


Dr. Hao WANG
Assistant professor
Department of Computer Science and Technology
Nanjing University (NJU), China

Date & Time

21 Aug 2015 (Friday) 11:00 - 12:00


E11-4045 (University of Macau)

Organized by

Department of Computer and Information Science


Dr. Hao Wang is currently an assistant professor at the Department of Computer Science and Technology, Nanjing University (NJU), China. He received his Bachelor's Degree in Mathematics from the Department of Mathematics, NJU in 2005 and Master's Degree in Computer Science from the Department of Computer Science and Technology, NJU in 2008. He received his PhD from the Department of Computer Science, The University of Hong Kong (HKU) in 2014. His current research interests include many aspects of Data Management and Machine Learning, with recent focus on rank-aware query processing / data indexing, recommender systems / location-based social networking, lifelong reinforcement learning, as well as big data analysis.