NDLI: Customized tour recommendations in urban areas

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Customized tour recommendations in urban areas

Content Provider	ACM Digital Library
Author	Terzi, Evimaria Lappas, Theodoros Gionis, Aristides Pelechrinis, Konstantinos
Abstract	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.
Starting Page	313
Ending Page	322
Page Count	10
File Format	PDF
ISBN	9781450323512
DOI	10.1145/2556195.2559893
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-02-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Urban computing Tour recommendations
Content Type	Text
Resource Type	Article

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