Trust and Reputation Modelling for Tourism Recommendations Supported by Crowdsourcing

  • Fátima Leal
  • Benedita Malheiro
  • Juan Carlos Burguillo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 745)

Abstract

Tourism crowdsourcing platforms have a profound influence on the tourist behaviour particularly in terms of travel planning. Not only they hold the opinions shared by other tourists concerning tourism resources, but, with the help of recommendation engines, are the pillar of personalised resource recommendation. However, since prospective tourists are unaware of the trustworthiness or reputation of crowd publishers, they are in fact taking a leap of faith when then rely on the crowd wisdom. In this paper, we argue that modelling publisher Trust & Reputation improves the quality of the tourism recommendations supported by crowdsourced information. Therefore, we present a tourism recommendation system which integrates: (i) user profiling using the multi-criteria ratings; (ii) k-Nearest Neighbours (k-NN) prediction of the user ratings; (iii) Trust & Reputation modelling; and (iv) incremental model update, i.e., providing near real-time recommendations. In terms of contributions, this paper provides two different Trust & Reputation approaches: (i) general reputation employing the pairwise trust values using all users; and (ii) neighbour-based reputation employing the pairwise trust values of the common neighbours. The proposed method was experimented using crowdsourced datasets from Expedia and TripAdvisor platforms.

Keywords

Crowdsourcing Trust & Reputation Rating prediction Tourism 

Notes

Acknowledgements

This work was partially financed by: (i) the European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalisation – COMPETE 2020 Programme – within project POCI-01-0145-FEDER-006961, and by National Funds through Fundação para a Ciência e a Tecnologia (FCT) – the Portuguese Foundation for Science and Technology – as part of project UID/EEA/50014/2013; and (ii) ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Fátima Leal
    • 1
    • 3
  • Benedita Malheiro
    • 2
    • 3
  • Juan Carlos Burguillo
    • 1
  1. 1.EET/UVigo – School of Telecommunication EngineeringUniversity of VigoVigoSpain
  2. 2.ISEP/IPP – School of EngineeringPolytechnic Institute of PortoPortoPortugal
  3. 3.INESC TECPortoPortugal

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