Content-Aware Point of Interest Recommendation on Location-Based Social Networks


  • Huiji Gao Arizona State University
  • Jiliang Tang Arizona State University
  • Xia Hu Arizona State University
  • Huan Liu Arizona State University



POI Recommendation, Location-based Social Networks, Content Aware


The rapid urban expansion has greatly extended the physical boundary of users' living area and developed a large number of POIs (points of interest). POI recommendation is a task that facilitates users' urban exploration and helps them filter uninteresting POIs for decision making. While existing work of POI recommendation on location-based social networks (LBSNs) discovers the spatial, temporal, and social patterns of user check-in behavior, the use of content information has not been systematically studied. The various types of content information available on LBSNs could be related to different aspects of a user's check-in action, providing a unique opportunity for POI recommendation. In this work, we study the content information on LBSNs w.r.t. POI properties, user interests, and sentiment indications. We model the three types of information under a unified POI recommendation framework with the consideration of their relationship to check-in actions. The experimental results exhibit the significance of content information in explaining user behavior, and demonstrate its power to improve POI recommendation performance on LBSNs.




How to Cite

Gao, H., Tang, J., Hu, X., & Liu, H. (2015). Content-Aware Point of Interest Recommendation on Location-Based Social Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).



Main Track: Machine Learning Applications