Multi-criteria Ratings for Recommender Systems: An Empirical Analysis in the Tourism Domain

  • Matthias Fuchs
  • Markus Zanker
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 123)


Most recommendation systems require some form of user feedback such as ratings in order to make personalized propositions of items. Typically ratings are unidimensional in the sense of consisting of a scalar value that represents the user’s appreciation for the rated item. Multi-criteria ratings allow users to express more differentiated opinions by allowing separate ratings for different aspects or dimensions of an item. Recent approaches of multi-criteria recommender systems are able to exploit this multifaceted user feedback and make personalized propositions that are more accurate than recommendations based on unidimensional rating data. However, most proposed multi-criteria recommendation algorithms simply exploit the fact that a richer feature space allows building more accurate predictive models without considering the semantics and available domain expertise. This paper contributes on the latter aspects by analyzing multi-criteria ratings from the major etourism platform, TripAdvisor, and structuring raters’ overall satisfaction with the help of a Penalty-Reward Contrast analysis. We identify that several a-priori user segments significantly differ in the way overall satisfaction can be explained by multi-criteria rating dimensions. This finding has implications for practical algorithm development that needs to consider different user segments.


Customer Satisfaction Recommender System Customer Segment Business Tourist Quality Domain 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems An Introduction. Cambridge University Press (2010)Google Scholar
  2. 2.
    Zanker, M., Jessenitschnig, M., Schmid, W.: Preference reasoning with soft constraints in constraint-based recommender systems. Constraints 15(4), 574–595 (2010)CrossRefGoogle Scholar
  3. 3.
    Ricci, F., Venturini, A., Cavada, D., Mirzadeh, N., Blaas, D., Nones, M.: Product Recommendation with Interactive Query Management and Twofold Similarity. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 479–493. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.): Handbook on Recommender Systems. Springer (2011)Google Scholar
  5. 5.
    Zanker, M., Bricman, M., Gordea, S., Jannach, D., Jessenitschnig, M.: Persuasive Online-Selling in Quality and Taste Domains. In: Bauknecht, K., Pröll, B., Werthner, H. (eds.) EC-Web 2006. LNCS, vol. 4082, pp. 51–60. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Jannach, D., Zanker, M., Fuchs, M.: Constraint-based recommendation in tourism: A multi-perspective case study. Information Technology & Tourism 11(2), 139–155 (2009)CrossRefGoogle Scholar
  7. 7.
    Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems 23(1), 103–145 (2005)CrossRefGoogle Scholar
  8. 8.
    Adomavicius, G., Kwon, Y.: New recommendation techniques for multicriteria rating systems. IEEE Intelligent Systems 22, 48–55 (2007)CrossRefGoogle Scholar
  9. 9.
    Jannach, D., Gedikli, F., Karakaya, Z., Juwig, O.: Recommending hotels based on multi-dimensional customer ratings. In: International Conference on Information and Communication Technologies in Tourism (ENTER), pp. 320–331. Springer (2012)Google Scholar
  10. 10.
    Kano, N.: Attractive Quality and Must-be Quality. Hinshitsu: The Journal of the Japanese Society for Quality Control 14(2), 39–48 (1984)Google Scholar
  11. 11.
    Brandt, R.D.: How service marketers can identify value enhancing service elements. Journal of Services Marketing 2(3), 35–41 (1988)CrossRefGoogle Scholar
  12. 12.
    O’Connor, P.: User-generated content and travel - a case study on In: International Conference on Information and Communication Technologies in Tourism (ENTER), pp. 47–58. Springer (2008)Google Scholar
  13. 13.
    Dippelreiter, B., Gruen, C., Poettler, M., Seidel, I., Berger, H., Dittenbach, M., Pesenhofer, A.: Online tourism communities on the path to web 2.0 - an evaluation of virtual communities in travel and tourism. Information Technology & Tourism 10(4), 329–353 (2007)CrossRefGoogle Scholar
  14. 14.
    Graebner, D., Zanker, M., Fliedl, G., Fuchs, M.: Classification of customer reviews based on sentiment analysis. In: 19th Conference on Information and Communication Technologies in Tourism (ENTER), pp. 460–470. Springer (2012)Google Scholar
  15. 15.
    Hosmer, D., Lemeshow, S.: Applied Logistic Regression, 2nd edn. Wiley, New York (2000)CrossRefGoogle Scholar
  16. 16.
    Klaus, P.: Quality Epiphenomenon: The Conceptual Understanding of Quality in Face-to-Face Service Encounters. In: The Service Encounter: Managing Employee Customer Interaction in Service Business, Lexington, pp. 17–33 (1985)Google Scholar
  17. 17.
    Weiermair, K., Fuchs, M.: Measuring tourist judgments on service quality. Annals of Tourism Research 26(4), 1004–1021 (1999)CrossRefGoogle Scholar
  18. 18.
    Hair, J.E., Anderson, R.E., Bubin, B.J., Tatham, R.L., Black, W.C.: Multivariate Data Analysis, 6th edn. Prentice-Hall, New York (2006)Google Scholar
  19. 19.
    Busacca, B., Padula, G.: Understanding the relationship between attribute performance and overall satisfaction: Theory, measurement and implications. Marketing Intelligence & Planning 23(6), 543–561 (2005)CrossRefGoogle Scholar
  20. 20.
    Johnston, R.: The determinants of service quality: Satisfiers and dis-satisfiers. International Journal of Service Industry Management 6(1), 53–71 (1995)CrossRefGoogle Scholar
  21. 21.
    Fuchs, M., Weiermair, K.: New perspectives on satisfaction research in tourism destinations. Tourism Review 58(3), 6–14 (2003)CrossRefGoogle Scholar
  22. 22.
    Matzler, K., Sauerwein, E.: The factor structure of customer satisfaction: An empirical test of the importance grid and the penalty-reward-contrast analysis. International Journal of Service Industry Management 13(4), 314–332 (2002)CrossRefGoogle Scholar
  23. 23.
    Matzler, K., Bailom, F., Hinterhuber, H., Renzl, B., Pichler, J.: The asymmetric relationship between attribute-level performance and overall customer satisfaction: A reconsideration of the importance-performance analysis. Industrial Marketing Management 33, 271–277 (2004)CrossRefGoogle Scholar
  24. 24.
    Fuchs, M., Weiermair, K.: Destination benchmarking: An indicator-system’s potential for exploring guest satisfaction. Journal of Travel Research 42, 212–225 (2004)CrossRefGoogle Scholar
  25. 25.
    Mikulic, J., Prebežac, D.: Prioritizing improvement of service attributes using impact range-performance analysis and impact-asymmetry analysis. Managing Service Quality 18(6), 559–576 (2008)CrossRefGoogle Scholar
  26. 26.
    Zanker, M., Ninaus, D.: Knowledgable explanations for recommender systems. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI/IAT), pp. 657–660. IEEE (2010)Google Scholar
  27. 27.
    Friedrich, G., Zanker, M.: A taxonomy for generating explanations in recommender systems. AI Magazine 32(3), 90–98 (2011)Google Scholar
  28. 28.
    Yoo, K.H., Gretzel, U., Zanker, M.: Persuasive Recommender Systems - Conceptual Background and Implications. Springer (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Matthias Fuchs
    • 1
  • Markus Zanker
    • 2
  1. 1.Mid Sweden UniversityÖstersundSweden
  2. 2.Alpen-Adria-Universität KlagenfurtKlagenfurtAustria

Personalised recommendations