Abstract
Recommender systems build user models to help users find the items they will find most interesting from among many available items. One way to build such a model is to ask the user to rate a selection of items. The choice of items selected affects the quality of the user model generated. In this paper, we explore the effects of letting the user participate in choosing the items that are used to develop the model. We compared three interfaces to elicit information from new users: having the system choose items for users to rate, asking the users to choose items themselves, and a mixed-initiative interface that combines the other two methods. We found that the two pure interfaces both produced accurate user models, but that directly asking users for items to rate increases user loyalty in the system. Ironically, this increased loyalty comes despite a lengthier signup process. The mixed-initiative interface is not a reasonable compromise as it created less accurate user models with no increase in loyalty.
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References
Allen, J. (1999) Mixed Initiative Interaction. Proceedings IEEE Intelligent Systems. 14(6), pp. 18–23.
Breese, J., Heckerman, D. and Kadie, C. (1998) Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Proceedings of UAI 1998. Pp. 43–52.
Brusilovsky, P. (1996) Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction. 6(2–3), pp. 87–129.
Fehr, E. and Gächter, S. (2000) Fairness and Retaliation: The Economics of Reciprocity. Journal of Economic Perspectives. 14(3), pp. 159–181.
Flemming, M. and Cohen, R. (1999) User Modeling in the Design of Interactive Interface Agents. Proceedings of User Modeling 1999. Pp. 67–76.
Fu, M. C., Hayes, C. C., and East, E. W. (1997) SEDAR: Expert Critiquing System for Flat and Low Slope Roof Design and Review. Journal of Computing in Civil Engineering. 11(1), pp. 60–69.
Herlocker, J., Konstan J, A., Borchers, A., and Riedl, J. (1999) An Algorithmic Framework for Performing Collaborative Filtering. Proceedings of SIGIR 1999. Pp. 230–237.
Horvitz, E. (1999) Principles of Mixed-Initiative User Interfaces. Proceedings of CHI 1999. Pp. 159–166.
Pennock, D., and Horvitz, E. (2000) Collaborative Filtering by Personality Diagnosis: A Hybrid Memory-and Model-based Approach. Proceedings of UAI 2000. Pp. 473–480.
Rashid, A. M., Albert, I., Cosley, D., Lam, S. K., McNee, S. M., Konstan, J. A., and Riedl, J. (2002) Getting to Know You: Learning New User Preferences in Recommender Systems. Proceedings of IUI 2002. Pp. 127–134.
Resnick, P., Iacovou, N., Sushak, M., Bergstrom, P., and Riedl, J. (1994) GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Proceedings of CSCW 94. Pp. 175–186.
Shneiderman, B. (1998) Designing the User Interface, third edition. Reading, MA: Addison Wesley.
Stolze, M. and Strobel, M. (2001) Utility-Based Decision Tree Optimization: A Framework for Adaptive Interviewing. Proceedings of User Modeling 2001. Pp. 105–116.
Terveen, L. G. (1993) Intelligent Systems as Cooperative Systems. International Journal of Intelligent Systems. 3,2–4, pp. 217–250.
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McNee, S.M., Lam, S.K., Konstan, J.A., Riedl, J. (2003). Interfaces for Eliciting New User Preferences in Recommender Systems. In: Brusilovsky, P., Corbett, A., de Rosis, F. (eds) User Modeling 2003. UM 2003. Lecture Notes in Computer Science(), vol 2702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44963-9_24
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DOI: https://doi.org/10.1007/3-540-44963-9_24
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