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Customized tour recommendations in urban areas

Gionis, A and Lappas, T and Pelechrinis, K and Terzi, E (2014) Customized tour recommendations in urban areas. In: UNSPECIFIED.

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The ever-increasing urbanization coupled with the unprecedented capacity to collect and process large amounts of data have helped to create the vision of intelligent urban environments. One key aspect of such environments is that they allow people to effectively navigate through their city. While GPS technology and route-planning services have undoubtedly helped towards this direction, there is room for improvement in intelligent urban navigation. This vision can be fostered by the proliferation of location-based social networks, such as Foursquare or Path, which record the physical presence of users in different venues through check-ins. This information can then be used to enhance intelligent urban navigation, by generating customized path recommendations for users. In this paper, we focus on the problem of recommending customized tours in urban settings. These tours are generated so that they consider (a) the different types of venues that the user wants to visit, as well as the order in which the user wants to visit them, (b) limitations on the time to be spent or distance to be covered, and (c) the merit of visiting the included venues. We capture these requirements in a generic definition that we refer to as the TourRec problem. We then introduce two instances of the TourRec problem, study their complexity, and propose efficient algorithmic solutions. Our experiments on real data collected from Foursquare demonstrate the efficacy of our algorithms and the practical utility of the reported recommendations. © 2014 ACM.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Gionis, A
Lappas, T
Pelechrinis, Kkpele@pitt.eduKPELE0000-0002-6443-3935
Terzi, E
Date: 1 January 2014
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: WSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining
Page Range: 313 - 322
Event Type: Conference
DOI or Unique Handle: 10.1145/2556195.2559893
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Telecommunications
Refereed: Yes
ISBN: 9781450323512
Date Deposited: 19 Jun 2014 15:11
Last Modified: 09 May 2020 14:55


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