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Personalized Hybrid Recommendations for Daily Activities in a Tourist Destination

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Ambient Intelligence – Software and Applications –, 9th International Symposium on Ambient Intelligence (ISAmI2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 806))

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Abstract

Valuable recommendations are not effortless to receive in a tourist destination [18]. Considering the daily routine of a person on vacation in a tourist destination, a hybrid Recommender System for a mobile app is proposed. A hybrid system helps in unifying the best aspects of different recommendation algorithms while simultaneously minimizing the drawbacks of the individual algorithms. It is capable of providing personalized, diverse and serendipitous recommendations for the stay in a tourist destination and suggests places to dine, to relax and possibilities for sports activities. As input for the algorithm, the information needs of tourists were examined conducting qualitative studies in an Alpine tourist destination. The proposed Recommender System, the results of the qualitative studies and the basic testing performed using initial data are presented.

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Notes

  1. 1.

    Accessed on August 8, 2017: https://www.statista.com/topics/962/global-tourism/.

  2. 2.

    www.booking.com, www.tripadvisor.com, and www.expedia.com accessed on August 8, 2017.

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Acknowledgement

This work was funded in part by Innosuisse - the Swiss Innovation Agency. The authors would also like to thank ipeak Infosystems for their support and for providing the data that made this work possible.

Author information

Authors and Affiliations

  1. Lucerne University of Applied Sciences and Arts, Suurstoffi 41b, 6343, Rotkreuz, Switzerland

    Tahir Majeed, Aline E. Stämpfli, Andreas Liebrich & René Meier

Authors
  1. Tahir Majeed
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  2. Aline E. Stämpfli
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  3. Andreas Liebrich
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  4. René Meier
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Corresponding author

Correspondence to Tahir Majeed .

Editor information

Editors and Affiliations

  1. ALGORITMI Centre/Departamento de Informatica, University of Minho, Braga, Portugal

    Paulo Novais

  2. Department of Computer Engineering, Chung-Ang University, South Korea, Korea (Republic of)

    Jason J. Jung

  3. Departamento de Informática y Automática, Facultad de Ciencias, Universidad de Salamanca, Salamanca, Spain

    Gabriel Villarrubia González

  4. Departamento de Sistemas Informáticos, University of Castilla-La Mancha, Albacete, Spain

    Antonio Fernández-Caballero

  5. Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, Spain

    Elena Navarro

  6. Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, Spain

    Pascual González

  7. Instituto Politécnico do Porto, Escola Superior de Tecnologia e Gestão, Felgueiras, Portugal

    Davide Carneiro

  8. ESTG, Politécnico do Porto and CRACS & INESC TEC, Porto, Portugal

    António Pinto

  9. Department of Computer Science, Dartmouth College, Hanover, NH, USA

    Andrew T. Campbell

  10. Department of Artificial Intelligence, Technical University of Madrid, Madrid, Spain

    Dalila Durães

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Majeed, T., Stämpfli, A.E., Liebrich, A., Meier, R. (2019). Personalized Hybrid Recommendations for Daily Activities in a Tourist Destination. In: Novais, P., et al. Ambient Intelligence – Software and Applications –, 9th International Symposium on Ambient Intelligence. ISAmI2018 2018. Advances in Intelligent Systems and Computing, vol 806. Springer, Cham. https://doi.org/10.1007/978-3-030-01746-0_18

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