Image-based Travel Recommender System for small tourist destinations

Most tour planning systems have similar three-step structures: definition of the tourist profile, evaluation of the Points of Interest (PoI), and route optimization. Having in mind that a picture paints a thousand words, this paper describes an approach that allows tourists specify their interests through a set of images, from which the system inferences their profile. Taken into account the choices made by the visitor, the system infers a dynamic profile. Furthermore, the system calculates a list of resources that match the profile with the destination tourist resources. Each resource is associated with a weight which indicates the utility of that resource to the profile of the visitor. Thus, visitors obtain personalized tourism recommendations based on their preferences as a result.

[1]  Jesus Boticario,et al.  samap: An user-oriented adaptive system for planning tourist visits , 2008, Expert Syst. Appl..

[2]  Owen Conlan,et al.  VUMA: A Visual User Modelling Approach for the Personalisation of Adaptive Systems , 2008, AH.

[3]  D. Fesenmaier,et al.  Destination Recommendation Systems: Behavioural Foundations and Applications , 2006 .

[4]  Nalin Sharda,et al.  Tourism Informatics: Visual Travel Recommender Systems, Social Communities, and User Interface Design , 2009 .

[5]  Carlo Strapparava,et al.  Adaptive Hypermedia and Adaptive Web-Based Systems, 5th International Conference, AH 2008, Hannover, Germany, July 29 - August 1, 2008. Proceedings , 2008, AH.

[6]  Dirk Van Oudheusden,et al.  Iterated local search for the team orienteering problem with time windows , 2009, Comput. Oper. Res..

[7]  Francesco Ricci,et al.  Case Base Querying for Travel Planning Recommendation , 2001, J. Inf. Technol. Tour..

[8]  Helmut Berger,et al.  Photo-Based User Profiling for Tourism Recommender Systems , 2007, EC-Web.

[9]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[10]  Nalin Sharda,et al.  Developing Visual Tourism Recommender Systems , 2007 .

[11]  Nalin Sharda,et al.  Developing a Visualisation Tool for Tour Planning , 2006, ENTER.

[12]  Francesco Ricci,et al.  DIETORECS: Travel Advisory for Multiple Decision Styles , 2003, ENTER.

[13]  Olatz Arbelaitz,et al.  Intelligent Routing System for a Personalised Electronic Tourist Guide , 2009, ENTER.

[14]  Helmut Berger,et al.  Quo Vadis Homo Turisticus? Towards a Picture-based Tourist Profiler , 2007, ENTER.

[15]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[16]  Alfred Kobsa,et al.  Generic User Modeling Systems , 2001, User modeling and user-adapted interaction.

[17]  D. Fesenmaier,et al.  Case-based travel recommendations. , 2006 .

[18]  Michael J. Pazzani,et al.  User Modeling for Adaptive News Access , 2000, User Modeling and User-Adapted Interaction.

[19]  Steffen Staab,et al.  Intelligent Systems for Tourism , 2002, IEEE Intell. Syst..

[20]  Ulrike Gretzel,et al.  Tell Me Who You Are and I Will Tell You Where to Go: Use of Travel Personalities in Destination Recommendation Systems , 2004, J. Inf. Technol. Tour..

[21]  Peretz Shoval,et al.  Information Filtering: Overview of Issues, Research and Systems , 2001, User Modeling and User-Adapted Interaction.

[22]  Josep Lluís de la Rosa i Esteva,et al.  A Taxonomy of Recommender Agents on the Internet , 2003, Artificial Intelligence Review.

[23]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.