Abstract
This paper describes LCW, a procedure for learning customer preferences by observing customers’ selections from return sets. An empirical evaluation on simulated customer behavior indicated that an uninformed hypothesis about customer weights leads to low ranking accuracy unless customers place some importance on almost all features or the total number of features is quite small. In contrast, LCW’s estimate of the mean preferences of a customer population improved as the number of customers increased, even for larger numbers of features of widely differing importance. This improvement in the estimate of mean customer preferences led to improved prediction of individual customer’s rankings, irrespective of the extent of variation among customers and whether a single or multiple retrievals were permitted. The experimental results suggest that the return set that optimizes benefit may be smaller for customer populations with little variation than for customer populations with wide variation.
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
K. Branting and P. Broos. Automated acquisition of user preferences. International Journal of Human-Computer Studies, 46:55–77, 1997.
A. Bonzano, P. Cunningham, and B. Smyth. Using introspective learning to improve retrieval in CBR: A case study in air traffic control. In Proceedings of the Second International Conference on Case-Based Reasoning, pages 291–302, Providence, Rhode Island, July 25-27 1997. Springer.
R. Burke, K. Hammond, V. Kulyukin, S. Lytinen, N. Tomuro, and S. Schoenberg. Question answering from frequently-asked question files: Experiences with the FAQ nder system. Technical Report TR-97-05, University of Chicago, Department of Computer Science, 1997.
K. Branting. Active exploration in instance-based preference modeling. In Proceedings of the Third International Conference on Case-Based Reasoning (ICCBR-99), Lecture Notes in Artificial Intelligence 1650, Monastery Seeon, Germany, 1999.
L. Dent, J. Boticario, J. McDermott, T. Mitchell, and D. Zabowski. A personal learning apprentice. In Proceedings of Tenth National Conference on Artificial Intelligence, pages 96–103, San Jose, CA, July 12–16 1992. AAAI Press/MIT Press.
D. Goldberg, D. Nichols, B. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61–70, 1992.
J. Kolodner. Retrieval and Organizational Strategies in Conceptual Memory: a Computer Model. Lawrence Erlbaum Associates, Hillsdale, NJ, 1984.
R. Keeney and H. Raifia. Decisions with Multiple Objectives: Preferences and Value Tradeos. Cambridge University Press, second edition, 1993.
P. Maes. Agents that reduce work and information overload. Communications of the ACM, 37(7):31–40, 1994.
J. Nielson. Designing Web Usability. New Riders Publishing, Indianapolis, Indiana, USA, 2000.
J. Sweller, P. Chandler, P. T ierney, and M. Cooper. Cognitive load as a factor in the structuring of technical material. Journal of Experimental Psychology: General, pages 176–192, 1990.
M. Schumacher and T. Roth-Berghofer. Architecture for integration of CBR systems with databases for e-commerce. In Proceedings of the Seventh German Workshop on CBR (GWCBR’99), 1999.
D. Wettschereck and D. Aha. Weighting features. In Lecture Notes in Artificial Intelligence, pages 347–358, Sesimbra, Portugal, October 1995. Springer.
W. Wilke. Knowledge Management for Intelligent Sales Support in Electronic Commerce. PhD thesis, University of Kaiserslautern, 1999.
W. Wilke, M. Lenz, and S. Wess. Intelligent sales support with CBR. In M. Lenz, B. Bartsch-Spoerl, H.-D. Burkhard, and S. Wess, editors, Case-Based Reasoning Technology: from Foundations to Applications. LNAI 1400, volume 1400, pages 91–113. Springer, 1998.
Z. Zhang and Q. Yang. Dynamic refinement of feture weights using quantitative introspective learning. In International Joint Conference on Artificial Intelligence, pages 228–233. Morgan Kaufmann, August 1999.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Branting, L.K. (2001). Acquiring Customer Preferences from Return-Set Selections. In: Aha, D.W., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2001. Lecture Notes in Computer Science(), vol 2080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44593-5_5
Download citation
DOI: https://doi.org/10.1007/3-540-44593-5_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42358-4
Online ISBN: 978-3-540-44593-7
eBook Packages: Springer Book Archive