On planning sightseeing tours with TripBuilder

• The formal problem of planning a tourist visit as a fully-automatic two-step process.
• An unsupervised method for mining common patterns of movements of tourists.
• A comprehensive evaluation of TripBuilder.

We propose TripBuilder, an unsupervised framework for planning personalized sightseeing tours in cities. We collect categorized Points of Interests (PoIs) from Wikipedia and albums of geo-referenced photos from Flickr. By considering the photos as traces revealing the behaviors of tourists during their sightseeing tours, we extract from photo albums spatio-temporal information about the itineraries made by tourists, and we match these itineraries to the Points of Interest (PoIs) of the city. The task of recommending a personalized sightseeing tour is modeled as an instance of the Generalized Maximum Coverage (GMC) problem, where a measure of personal interest for the user given her preferences and visiting time-budget is maximized. The set of actual trajectories resulting from the GMC solution is scheduled on the tourist’s agenda by exploiting a particular instance of the Traveling Salesman Problem (TSP). Experimental results on three different cities show that our approach is effective, efficient and outperforms competitive baselines.