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

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 LB- SNs 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.

Authors

• Huiji Gao
• Jiliang Tang
• Xia Hu Rice University
• Huan Liu Institute of Information Science, Beijing Jiaotong University Visual Intellgence +X International Cooperation Joint Laboratory of MOE

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