An Economic Model of User Rating in an Online Recommender System
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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.
Keywords
Recommender System Behavioral Data User Rate User Behavior Initial UnderstandingPreview
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References
- 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.Bakos, J.Y.: Reducing buyer search costs: implications for electronic marketplaces. Manage. Sci. 43, 1676–1692 (1997)CrossRefzbMATHGoogle Scholar
- 3.Brusilovsky, P.: Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction 6, 87–129 (1996)CrossRefGoogle Scholar
- 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.Friedman, E.J., Resnick, P.: The social cost of cheap pseudonyms. Journal of Economics & Management Strategy 10, 173–199 (2001)CrossRefGoogle Scholar
- 6.Greene, W.H.: Econometric analysis, 4th edn. Prentice Hall/Upper Saddle River (2000)Google Scholar
- 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.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.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.Keser, C.: Experimental games for the design of reputation management systems. IBM Systems Journal 42, 498–506 (2003)CrossRefGoogle Scholar
- 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.Mas-Colell, A., Whinston, M.D., Green, J.R.: Microeconomic Theory. Oxford University Press, Oxford (1995)Google Scholar
- 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.Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40, 56–58 (1997)CrossRefGoogle Scholar
- 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.Wooldridge, J.M.: Introductory Econometrics: A Modern Approach, 2nd edn. South-Western College (2002)Google Scholar