Advanced Recommendation Models for Mobile Tourist Information

  • Annika Hinze
  • Saijai Junmanee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4275)


Personalized recommendations in a mobile tourist information system suffer from a number of limitations. Most pronounced is the amount of initial user information needed to build a user model. In this paper, we adopt and extend the basic concepts of recommendation paradigms by exploiting a user’s personal information (e.g., preferences, travel histories) to replace the missing information. The designed algorithms are embedded as recommendation services in our TIP prototype. We report on the results of our analysis regarding effectiveness and performance of the recommendation algorithms. We show how a number of limiting factors were successfully eliminated by our new recommender strategies.


Recommender System Collaborative Filter Similar User Recommendation Algorithm Travel History 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Annika Hinze
    • 1
  • Saijai Junmanee
    • 1
  1. 1.University of WaikatoNew Zealand

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