Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
Applying Recursive CBR for the Custumization of Structured Products in an Electronic Shop
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Learning User Preferences in Case-Based Software Reuse
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Improving Case-Based Recommendation: A Collaborative Filtering Approach
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
ITR: A Case-Based Travel Advisory System
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Acquiring Customer Preferences from Return-Set Selections
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Learning Feature Weights from Case Order Feedback
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
A Case-Based Reasoning View of Automated Collaborative Filtering
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Adaptive voting rules for k-nearest neighbors classifiers
Neural Computation
Product recommendation with interactive query management and twofold similarity
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Using evolution programs to learn local similarity measures
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Re-using implicit knowledge in short-term information profiles for context-sensitive tasks
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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
Retrieval and configuration of life insurance policies
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
An Analysis of Research Themes in the CBR Conference Literature
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Conversational Case-Based Recommendations Exploiting a Structured Case Model
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
CBR Supports Decision Analysis with Uncertainty
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Journal of Systems and Software
A hybrid similarity concept for browsing semi-structured product items
EC-Web'06 Proceedings of the 7th international conference on E-Commerce and Web Technologies
Intelligent product search with soft-boundary preference relaxation
Expert Systems with Applications: An International Journal
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Product recommender systems are a popular application and research field of CBR for several years now. However, almost all CBR-based recommender systems are not case-based in the original view of CBR, but just perform a similarity-based retrieval of product descriptions. Here, a predefined similarity measure is used as a heuristic for estimating the customers' product preferences. In this paper we propose an extension of these systems, which enables case-based learning of customer preferences. Further, we show how this approach can be combined with existing approaches for learning the similarity measure directly. The presented results of a first experimental evaluation demonstrate the feasibility of our novel approach in an example test domain.