Case-based reasoning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Communications of the ACM
Generalized vector spaces model in information retrieval
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning Support for Online Catalog Sales
IEEE Internet Computing
Adaptive Assistants for Customized E-Shopping
IEEE Intelligent Systems
Considering Decision Cost During Learning of Feature Weights
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Learning a Local Similarity Metric for Case-Based Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research 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
Optimizing Return-Set Size for Requirements Satisfaction and Cognitive Load
ISEC '02 Proceedings of the Third International Symposium on Electronic Commerce
Learning Feature Weights from Customer Return-Set Selections
Knowledge and Information Systems
A knowledge-based product recommendation system for e-commerce
International Journal of Intelligent Information and Database Systems
HYREC: a hybrid recommendation system for e-commerce
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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Learning the preferences of users is an important problem in e-commerce research. This paper presents a system for that purpose, and it is primarily based on weight vectors. Learning is incorporated in the form of refining the weights. The system is sensitive to the users' change of trend and is implemented for labor profile domain. An empirical evaluation has been conducted in a simulated environment. The results proved the following: (1) The system converges if the user populations have some common preferences, (2) The system detects and adapts to a change of trend, and (3) It takes more time to converge in the case of more weights.