Guandong Xu - Leveraging user review and numeric rating order to capture user preference in location-based social networks

With the pervasive use of mobile devices, Location Based Social Networks (LBSNs) have emerged and become popular in past years. These LBSNs, allowing their users to share personal experiences and opinions on visited merchants, have very rich and useful information which enables a new breed of location-based services, namely, Merchant Recommendation. Existing techniques for merchant recommendation simply rely on rating data and treat each merchant as an item, and apply conventional recommendation algorithms, e.g., Collaborative Filtering, to recommend merchants to a target user. However due to the individual difference existing in user rating, rating values themselves do not give exact preferences of user. On the other hand, apart from user rating data, user reviews which convey accurate user preference information are inadequately considered in existing techniques. In this talk, we report our recent work addressing above two problems by 1) analyzing user reviews to discover user preferences in different aspects; and 2) leveraging the numeric order of ratings given by a user within a certain period to capture user real preference based on utility theory. We conduct experiments to evaluate the proposed approaches in terms of effectiveness, efficiency and cold-start using two real-world datasets. The experimental results show that our approaches outperform the state-of-the-art methods.