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Data Mining and Knowledge Discovery

, Volume 31, Issue 5, pp 1157–1188 | Cite as

Tour recommendation for groups

  • Aris Anagnostopoulos
  • Reem Atassi
  • Luca Becchetti
  • Adriano Fazzone
  • Fabrizio Silvestri
Article
Part of the following topical collections:
  1. Journal Track of ECML PKDD 2017

Abstract

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.

Keywords

Group recommendation Tour recommendation for groups Orienteering problem 

Notes

Acknowledgements

We thank Fabrizio Grandoni for useful discussions on the problem complexity. We also thank Microsoft for awarding us with credits on their Azure cloud-computing platform, providing us in this way the required infrastructure to run our experiments. Finally, we want to thank the anonymous reviewers, whose comments helped to improve significantly our paper. This research was partially supported by the Google Focused Research Award “Algorithms for Large-Scale Data Analysis” and by the EU FET project MULTIPLEX 317532.

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Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.Sapienza University of RomeRomeItaly
  2. 2.ISTI CNRPisaItaly

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