Advertisement

The Impact of Rating Scales on User’s Rating Behavior

  • Cristina Gena
  • Roberto Brogi
  • Federica Cena
  • Fabiana Vernero
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)

Abstract

As showed in a previous work, different users show different preferences with respect to the rating scales to use for evaluating items in recommender systems. Thus in order to promote users’ participation and satisfaction with recommender systems, we propose to allow users to choose the rating scales to use. Thus, recommender systems should be able to deal with ratings coming from heterogeneous scales in order to produce correct recommendations. In this paper we present two user studies that investigate the role of rating scales on user’s rating behavior, showing that the rating scales have their own “personality” and mathematical normalization is not enough to cope with mapping among different rating scales.

Keywords

Recommender System Neutral Position Side Dish Mathematical Normalization Visual Metaphor 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowl. and Data Eng. 17, 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Amoo, T., Friedman, H.H.: Do Numeric Values Influence Subjects Responses to Rating Scales? Journal of International Marketing and Marketing Research 26, 41–46 (2001)Google Scholar
  3. 3.
    Cena, F., Vernero, F., Gena, C.: Towards a customization of rating scales in adaptive systems. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 369–374. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Churchill, J., Gilbert, A., Peter, J.P.: Research design effects on the reliability of rating scales: A meta-analysis. Journal of Marketing Research 21(4), 360–375 (1984)CrossRefGoogle Scholar
  5. 5.
    Cosley, D., Lam, S.K., Albert, I., Konstan, J.A., Riedl, J.: Is seeing believing?: how recommender system interfaces affect users’ opinions. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2003, pp. 585–592. ACM, New York (2003)Google Scholar
  6. 6.
    Friedman, H.H., Amoo, T.: Rating the rating scales. Journal of Marketing Management 9(3), 114–123 (1999)Google Scholar
  7. 7.
    Garland, R.: The Mid-Point on a Rating Scale: Is it Desirable. Marketing Bulletin 2, 66–70 (1991)Google Scholar
  8. 8.
    Goldberg, K.Y., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Inf. Retr. 4(2), 133–151 (2001)CrossRefzbMATHGoogle Scholar
  9. 9.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  10. 10.
    Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 1995, pp. 194–201. ACM Press/Addison-Wesley Publishing Co., New York, NY, USA (1995)Google Scholar
  11. 11.
    Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Swearingen, K., Sinha, R.: Interaction design for recommender systems. In: Proceedings of Designing Interactive Systems 2002. ACM Press, New York (2002)Google Scholar
  13. 13.
    van Barneveld, J., van Setten, M.: Designing Usable Interfaces for TV Recommender Systems. In: Ardissono, L., Kobsa, A., Maybury, M. (eds.) Personalized Digital Television. Targeting Programs to Individual Users, pp. 259–285. Kluwer Academic Publishers, Dordrecht (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cristina Gena
    • 1
  • Roberto Brogi
    • 1
  • Federica Cena
    • 1
  • Fabiana Vernero
    • 1
  1. 1.Dipartimento di InformaticaUniversità di TorinoTorinoItaly

Personalised recommendations