Tourismo: A User-Preference Tourist Trip Search Engine

  • Gregor JosséEmail author
  • Klaus Arthur Schmid
  • Andreas Züfle
  • Georgios Skoumas
  • Matthias Schubert
  • Dieter Pfoser
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)


In this demonstration we re-visit the problem of finding an optimal route from location A to B. Currently, navigation systems compute shortest, fastest, most economic routes or any combination thereof. More often than not users want to consider “soft” qualitative metrics such as popularity, scenic value, and general appeal of a route. Routing algorithms have not (yet) been able to appreciate, measure, and evaluate such qualitative measures. Given the emergence of user-generated content, data exists that records user preference. This work exploits user-generated data, including image data, text data and trajectory data, to estimate the attractiveness of parts of the spatial network in relation to a particular user. We enrich the spatial network dataset by quantitative scores reflecting qualitative attractiveness. These scores are derived from a user-specific self-assessment (“On vacation I am interested in: family entertainment, cultural activities, exotic food”) and the selection of a respective subset of existing POIs. Using the enriched network, our demonstrator allows to perform a bicriterion optimal path search, which optimizes both travel time as well as the attractiveness of the route. Users will be able to choose from a whole skyline of alternative routes based on their preference. A chosen route will also be illustrated using user-generated data, such as images, textual narrative, and trajectories, i.e., data that showcase attractiveness and hopefully lead to a perfect trip.


Road Network Trajectory Data Spatial Network Path Query Route Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research has received funding from the Shared E-Fleet project (in the IKTII program) by the BMWi (grant no. 01ME12107), from the DFG (grant no. RE 266/5-1), from the DAAD supported by the BMBF (grant no. 57052426). Dieter Pfoser has been partially supported by NGA NURI (grant no. HM02101410004).


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Gregor Jossé
    • 1
    Email author
  • Klaus Arthur Schmid
    • 1
  • Andreas Züfle
    • 1
  • Georgios Skoumas
    • 2
  • Matthias Schubert
    • 1
  • Dieter Pfoser
    • 3
  1. 1.Ludwig-Maximilians-Universität MünchenMunichGermany
  2. 2.National Technical University of AthensAthensGreece
  3. 3.George Mason UniversityFairfaxUSA

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