Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Agents that reduce work and information overload
Communications of the ACM
Designing Web Usability: The Practice of Simplicity
Designing Web Usability: The Practice of Simplicity
Active Exploration in Instance-Based Preference Modeling
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Using Introspective Learning to Improve Retrieval in CBR: A Case Study in Air Traffic Control
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Intelligent Sales Support with CBR
Case-Based Reasoning Technology, From Foundations to Applications
Dynamic Refinement of Feature Weights Using Quantitative Introspective Learning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Weight-Vector Based Approach for Product Recommendation in E-commerce
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Defining Similarity Measures: Top-Down vs. Bottom-Up
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Learning Similarity Functions from Qualitative Feedback
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Learning fuzzy rules for similarity assessment in case-based reasoning
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Learning similarity measures: a formal view based on a generalized CBR model
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Completeness criteria for retrieval in recommender systems
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Combining case-based and similarity-based product recommendation
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Fuzzy rule-based similarity model enables learning from small case bases
Applied Soft Computing
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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.