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Description:
It is important to learn the characteristics of travelers and touristic regions when trying to generate recommendations for destinations to users. In this work, we first present a data-driven method to mine trips from location-based social networks to understand how tourists travel the world. These trips are quantified using a number of metrics to capture the underlying mobility patterns. We then present two applications that utilize the mined trips. The first one is an approach for clustering travelers in two case studies, one of Twitter and another of Foursquare, where the pure mobility metrics are enriched with social aspects, i.e., the kinds of venues into which the users checked-in. Clustering 133,614 trips from Twitter, we obtain three distinct clusters. In the Foursquare data set, however, six clusters can be determined. The second application area is the spatial clustering of destinations around the world. These discovered regions are solely formed by the mobility patterns of the trips and are, thus, independent of administrative regions such as countries. We identify 942 regions as destinations that can be directly used as a region model of a destination recommender system. This paper is the extended version of the conference article “Characterisation of Traveller Types Using Check-in Data from Location-Based Social Networks” presented at the 26th Annual ENTER eTourism Conference held from January 19 to February 1, 2019 in Nicosia, Cyprus.