Abstract
Users’ critiques to the current recommendation form a crucial feedback mechanism for refining their preference models and improving a system’s accuracy in recommendations that may better interest the user. In this paper, we present a novel approach to assist users in making critiques according to their stated and potentially hidden preferences. This approach is derived from our previous work on critique generation and organization techniques. Based on a collection of real user data, we conducted an experiment to compare our approach with three existing critique generation systems. Results show that our preference-based organization interface achieves the highest level of prediction accuracy in suggesting users’ intended critiques and recommendation accuracy in locating users’ target choices. In addition, it can potentially most efficiently save real users’ interaction effort in decision making.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proc. ACM SIGMOD, pp. 207–216 (1993)
Burke, R.D., Hammond, K.J., Young, B.C.: The FindMe Approach to Assisted Browsing. IEEE Expert: Intelligent Systems and Their Applications 12(4), 32–40 (1997)
Chen, L., Pu, P.: Evaluating Critiquing-based Recommender Agents. In: Proc. 21st AAAI, pp. 157–162 (2006)
Chen, L., Pu, P.: Hybrid Critiquing-based Recommender Systems. In: Proc. IUI, pp. 22–31 (2007)
Keeney, R., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Cambridge University Press, Cambridge (1976)
McGinty, L., Smyth, B.: On the Role of Diversity in Conversational Recommender Systems. In: Proc. 5th ICCBR, pp. 276–290 (2003)
Payne, J.W., Bettman, J.R., Johnson, E.J.: The Adaptive Decision Maker. Cambridge University Press, Cambridge (1993)
Pu, P., Chen, L.: Integrating Tradeoff Support in Product Search Tools for e-commerce Sites. In: Proc. 6th ACM EC, pp. 269–278 (2005)
Pu, P., Chen, L.: Trust Building with Explanation Interfaces. In: Proc. IUI, pp. 93–100 (2006)
Pu, P., Kumar, P.: Evaluating Example-Based Search Tools. In: Proc. 5th ACM EC, pp. 208–217 (2004)
Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Dynamic Critiquing. In: Proc. 7th ECCBR, pp. 763–777 (2004)
Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Incremental Critiquing. In: Proc. 24th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp. 101–114 (2004)
Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Explaining Compound Critiques. Artificial Intelligence Review, vol. 24(2) (2005)
Thompson, C.A., Goker, M.H., Langley, P.: A Personalized System for Conversational Recommendations. Journal of Artificial Intelligence Research 21, 393–428 (2004)
Viappiani, P., Faltings, B., Pu, P.: Preference-based Search using Example-Critiquing with Suggestions. Journal of Artificial Intelligence Research (to appear, 2007)
Zhang, J., Pu, P.: A Comparative Study of Compound Critique Generation in Conversational Recommender Systems. In: Proc. AH, pp. 234–243 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, L., Pu, P. (2007). Preference-Based Organization Interfaces: Aiding User Critiques in Recommender Systems. In: Conati, C., McCoy, K., Paliouras, G. (eds) User Modeling 2007. UM 2007. Lecture Notes in Computer Science(), vol 4511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73078-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-73078-1_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73077-4
Online ISBN: 978-3-540-73078-1
eBook Packages: Computer ScienceComputer Science (R0)