Personalized travel route recommendation with skyline query

Traveling has received much more attention with the rising living standard in recent years. Therefore, it is essential to make a good recommendation on how to choose a high-quality travel route for travelers. Besides, with the exponential increase of smart devices equipped with a GPS module and the advancement of social networks, Location-based social networks (LBSNs) have come into being. As a consequence, a great many check-in data are generated in human's routine life everyday, which can be used for future travel route recommendation. In this paper, we propose a personalized travel route recommendation scheme by mining the collected check-in data. Our method is based on applying skyline queries to recommend. Skyline is a set of points that are not dominated by other points on all dimensions. In our schemes, in order to search attractions to be recommended to users, we employ a variation of skyline queries which is called as K-dominating queries. It retrieves top-K points dominating the largest number of other points from the database. Then we choose top-K attractions by utilizing K-dominating queries and produce candidate routes. We also analyze the recommendation with considering individual preference. Finally, experiments are conducted to exhibit the performance of our schemes.