Consider a group of people who are visiting a major touristic city, such as NY, Paris, or Rome. It is reasonable to assume that each member of the group has his or her own interests or preferences about places to visit, which in general may differ from those of other members. Still, people almost always want to hang out together and so the following question naturally arises: What is the best tour that the group could perform together in the city? This problem underpins several challenges, ranging from understanding people’s expected attitudes towards potential points of interest, to modeling and providing good and viable solutions. Formulating this problem is challenging because of multiple competing objectives. For example, making the entire group as happy as possible in general conflicts with the objective that no member becomes disappointed. In this paper, we address the algorithmic implications of the above problem, by providing various formulations that take into account the overall group as well as the individual satisfaction and the length of the tour. We then study the computational complexity of these formulations, we provide effective and efficient practical algorithms, and, finally, we evaluate them on datasets constructed from real city data.
2017, DATA MINING AND KNOWLEDGE DISCOVERY, Pages 1157-1188 (volume: 31)
Tour recommendation for groups (01a Articolo in rivista)
Anagnostopoulos Aris, Atassi Reem, Becchetti Luca, Fazzone Adriano, Silvestri Fabrizio
Gruppo di ricerca: Algorithms and Data Science