Data-driven Destination Recommender Systems
This thesis explores various aspects of the destination recommendation domain, namely mobility analyses about which types of travelers can be observed in location-based social media data, how these groups perform in point-of-interest recommendation, how long travelers should stay at a destination, and which data sources are suitable to characterize destinations. Finally, we propose a city recommender system that supports users making the trade-offs involved in their travel choices.