Personalized POI Recommendation on Location-Based Social Networks Disseration Prospectus

The rapid urban expansion has greatly extended the physical boundary of our living area, along with a large number of POIs (points of interest) being developed. A POI is a specific location (e.g., hotel, restaurant, theater, mall) that a user may find useful or interesting. When exploring the city and neighborhood, the increasing number of POIs could enrich people’s daily life, providing them with more choices of life experience than before, while at the same time also brings the problem of “curse of choices”, resulting in the difficulty for a user to make a satisfied decision on “where to go ” in an efficient way. Personalized POI recommendation is a task proposed on purpose of helping users filter out uninteresting POIs and reduce time in decision making, which could also benefit virtual marketing. Developing POI recommender systems requires observation of human mobile behavior w.r.t. real-world POIs, which is infeasible with traditional mobile data. However, the recent development of location-based social networks (LBSNs) provides such observation. Typical location-based social networking sites allow users to “check in ” at POIs with smartphones, leave tips and share that experience with their online friends. The increasing number of LBSN users has generated large amounts of LBSN data, providing an unprecedented opportunity to study human mobile behavior for personalized POI recommendation in spatial, temporal, social