Travel route recommendation services that recommend a sequence of points-of-interest (POIs) for tourists are very useful in location-based social networks (LBSNs). Currently, most of the work that addresses this task are focusing on personalization and POI features, which estimate user-location relations while rarely considering transitions, i.e., the relationships between locations. To this end, we propose a latent factorization model that learns transition patterns with enhanced spatial-temporal features between locations. Furthermore, we recommend travel routes by combining knowledge on locations and transitions. Experimental results with public datasets reveal that our approaches improve upon the performance of conventional methods.

Key words: travel route recommendation, LBSN, sightseeing, matrix factorization