An Economic Model of User Rating in an Online Recommender System

  • F. Maxwell Harper
  • Xin Li
  • Yan Chen
  • Joseph A. Konstan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3538)

Abstract

Economic modeling provides a formal mechanism to understand user incentives and behavior in online systems. In this paper we describe the process of building a parameterized economic model of user-contributed ratings in an online movie recommender system. We constructed a theoretical model to formalize our initial understanding of the system, and collected survey and behavioral data to calibrate an empirical model. This model explains 34% of the variation in user rating behavior. We found that while economic modeling in this domain requires an initial understanding of user behavior and access to an uncommonly broad set of user survey and behavioral data, it returns significant formal understanding of the activity being modeled.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ashenfelter, O., Krueger, A.B.: Estimates of the economic returns to schooling from a new sample of twins. American Economic Review 84, 1157–1173 (1994)Google Scholar
  2. 2.
    Bakos, J.Y.: Reducing buyer search costs: implications for electronic marketplaces. Manage. Sci. 43, 1676–1692 (1997)CrossRefMATHGoogle Scholar
  3. 3.
    Brusilovsky, P.: Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction 6, 87–129 (1996)CrossRefGoogle Scholar
  4. 4.
    Butler, B.S.: Membership size, communication activity, and sustainability: A resource-based model of online social structures. Info. Sys. Research 12, 346–362 (2001)CrossRefGoogle Scholar
  5. 5.
    Friedman, E.J., Resnick, P.: The social cost of cheap pseudonyms. Journal of Economics & Management Strategy 10, 173–199 (2001)CrossRefGoogle Scholar
  6. 6.
    Greene, W.H.: Econometric analysis, 4th edn. Prentice Hall/Upper Saddle River (2000)Google Scholar
  7. 7.
    Grudin, J.: Why CSCW applications fail: problems in the design and evaluation of organizational interfaces. In: Proceedings of CSCW 1988, pp. 85–93. ACM Press, New York (1988)Google Scholar
  8. 8.
    Herlocker, J., Konstan, J.A., Riedl, J.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retr. 5, 287–310 (2002)CrossRefGoogle Scholar
  9. 9.
    Horvitz, E., Apacible, J.: Learning and reasoning about interruption. In: Proceedings of ICMI 2003, pp. 20–27. ACM Press, New York (2003)CrossRefGoogle Scholar
  10. 10.
    Keser, C.: Experimental games for the design of reputation management systems. IBM Systems Journal 42, 498–506 (2003)CrossRefGoogle Scholar
  11. 11.
    Levitt, S.D.: Using electoral cycles in police hiring to estimate the effect of police on crime. American Economic Review 87, 270–290 (1997)Google Scholar
  12. 12.
    Mas-Colell, A., Whinston, M.D., Green, J.R.: Microeconomic Theory. Oxford University Press, Oxford (1995)Google Scholar
  13. 13.
    McNee, S.M., Lam, S.K., Konstan, J.A., Riedl, J.: Interfaces for eliciting new user preferences in recommender systems. In: Proceedings of User Modeling 2003, pp. 178–187 (2003)Google Scholar
  14. 14.
    Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40, 56–58 (1997)CrossRefGoogle Scholar
  15. 15.
    Varian, H.R.: How to build an economic model in your spare time. In: Szenberg, M. (ed.) Passion and Craft, How Economists Work. University of Michigan Press (1995)Google Scholar
  16. 16.
    Wooldridge, J.M.: Introductory Econometrics: A Modern Approach, 2nd edn. South-Western College (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • F. Maxwell Harper
    • 1
  • Xin Li
    • 2
  • Yan Chen
    • 2
  • Joseph A. Konstan
    • 1
  1. 1.CommunityLabUniversity of MinnesotaMinneapolisUSA
  2. 2.CommunityLabUniversity of MichiganAnn ArborUSA

Personalised recommendations