Trust and Reputation Modelling for Tourism Recommendations Supported by Crowdsourcing

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


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.


Crowdsourcing Trust & Reputation Rating prediction Tourism 



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).


  1. 1.
    Aggarwal, C.C.: Neighborhood-based collaborative filtering. In: Recommender Systems, pp. 29–70. Springer (2016)CrossRefGoogle Scholar
  2. 2.
    Bedi, P., Agarwal, S.K., Jindal, V., et al.: MARST: multi-agent recommender system for e-tourism using reputation based collaborative filtering. In: International Workshop on Databases in Networked Information Systems, pp. 189–201. Springer (2014)CrossRefGoogle Scholar
  3. 3.
    Bustos, F., López, J., Julián, V., Rebollo, M.: STRS: social network based recommender system for tourism enhanced with trust. In: International Symposium on Distributed Computing and Artificial Intelligence 2008, (DCAI 2008), pp. 71–79. Springer (2009)Google Scholar
  4. 4.
    Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: RecSys 2010, pp. 39–46. ACM, Barcelona, September 2010Google Scholar
  5. 5.
    Doan, A., Ramakrishnan, R., Halevy, A.Y.: Crowdsourcing systems on the world-wide web. Commun. ACM 54(4), 86–96 (2011)CrossRefGoogle Scholar
  6. 6.
    Ekstrand, M.D., Riedl, J.T., Konstan, J.A., et al.: Collaborative filtering recommender systems. Found. Trends Hum. Comput. Interact. 4(2), 81–173 (2011)CrossRefGoogle Scholar
  7. 7.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  8. 8.
    Gretzel, U., Sigala, M., Xiang, Z., Koo, C.: Smart tourism: foundations and developments. Electron. Markets 25(3), 179–188 (2015)CrossRefGoogle Scholar
  9. 9.
    Gula, I.: Crowdsourcing in the tourism industry—using the example of ideas competitions in tourism destinations. In: ISCONTOUR 2013: Proceedings of the International Student Conference in Tourism Research, p. 147. BoD–Books on Demand (2013)Google Scholar
  10. 10.
    Howe, J.: The rise of crowdsourcing. Wired Mag. 14(6), 1–4 (2006)Google Scholar
  11. 11.
    Jøsang, A., Guo, G., Pini, M.S., Santini, F., Xu, Y.: Combining recommender and reputation systems to produce better online advice. In: International Conference on Modeling Decisions for Artificial Intelligence, pp. 126–138. Springer (2013)CrossRefGoogle Scholar
  12. 12.
    Jøsang, A., Ismail, R., Boyd, C.: A survey of trust and reputation systems for online service provision. Decis. Support Syst. 43(2), 618–644 (2007)CrossRefGoogle Scholar
  13. 13.
    Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 77–118. Springer (2015). Chapter 3CrossRefGoogle Scholar
  14. 14.
    Lakiotaki, K., Delias, P., Sakkalis, V., Matsatsinis, N.F.: User profiling based on multi-criteria analysis: the role of utility functions. Opera. Res. 9(1), 3–16 (2009)CrossRefGoogle Scholar
  15. 15.
    Leal, F., González-Vélez, H., Malheiro, B., Burguillo, J.C.: Profiling and rating prediction from multi-criteria crowd-sourced hotel rating. In: Proceedings of the 31th European Conference on Modelling and Simulation, ECMS 2017, pp. 576–582. ECMS (2017)Google Scholar
  16. 16.
    Leal, F., Malheiro, B., Burguillo, J.C.: Prediction and Analysis of Hotel Ratings from Crowd-Sourced Data, pp. 493–502. Springer, Cham (2017)Google Scholar
  17. 17.
    Leal, F., Malheiro, B., González-Vélez, H., Burguillo, J.C.: Trust-based modelling of multi-criteria crowdsourced data. Data Sci. Eng. (2017)CrossRefGoogle Scholar
  18. 18.
    Neuhofer, B.: Innovation through co-creation: towards an understanding of technology-facilitated co-creation processes in tourism. In: Open Tourism, pp. 17–33. Springer (2016)CrossRefGoogle Scholar
  19. 19.
    Neuhofer, B., Buhalis, D., Ladkin, A.: A typology of technology-enhanced tourism experiences. Int. J. Tourism Res. 16(4), 340–350 (2014)CrossRefGoogle Scholar
  20. 20.
    Richthammer, C., Weber, M., Pernul, G.: Reputation-enhanced recommender systems. In: IFIP International Conference on Trust Management, pp. 163–179. Springer (2017)CrossRefGoogle Scholar
  21. 21.
    Sedgwick, P.: Pearson’s correlation coefficient. Br. Med. J. 345, e4483 (2012)CrossRefGoogle Scholar
  22. 22.
    Takács, G., Pilászy, I., Németh, B., Tikk, D.: Scalable collaborative filtering approaches for large recommender systems. J. Mach. Learn. Res. 10, 623–656 (2009)Google Scholar
  23. 23.
    Veloso, B., Malheiro, B., Burguillo, J.C., Foss, J.: Personalised fading for stream data. In: SAC 2017, pp. 870–872. ACM, Marrakech, April 2017Google Scholar
  24. 24.
    Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: a rating regression approach. In: KDD 2010, pp. 783–792. ACM, Washington, July 2010Google Scholar

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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