Cross-Domain Point-of-Interest Recommendation In Location-based Social Networks

Recent years have witnessed the rapid growth of location-based social networks(LBSNs)such as Foursquare,Whrrl,Facebook Places.Point-of-interest(POI)recommendation is a significant task in LBSNs,since it can help users explore new places as well as assist third-party business to provide personalized services.Most of existing POI recommendation methods deals with the only single domain,whereas the use of information available from other application domains potentially provides better POI recommendation results.This is particularly relevant for the newly launched LBSNs,where little check-in information is available.In this work,to the best of our knowledge,we are the first to propose a cross-domain POI recommendation method in LBSNs and apply transfer learning for this purpose.To this end,we first incorporate both user and POI knowledge in auxiliary domains by discovering the principle coordinates.Next,we transfer them to the target domain and then make predictions using the transferred knowledge.The experimental results demonstrate high performance of our method and the merits of cross-domain POI recommendation in LBSNs.