Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Conversational Case-Based Reasoning
Applied Intelligence
Artificial Intelligence Review
Interactive Critiquing forCatalog Navigation in E-Commerce
Artificial Intelligence Review
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
Personalized Conversational Case-Based Recommendation
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Comparison-Based Recommendation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Reinforcement Learning Architecture for Web Recommendations
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Knowledge-based navigation of complex information spaces
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Knowledge discovery from user preferences in conversational recommendation
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Adaptive case-based reasoning using retention and forgetting strategies
Knowledge-Based Systems
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Personalizing the product recommendation task is a major focus of research in the area of conversational recommender systems. Conversational case-based recommender systems help users to navigate through product spaces, alternatively making product suggestions and eliciting users feedback. Critiquing is a common form of feedback and incremental critiquing-based recommender system has shown its efficiency to personalize products based primarily on a quality measure. This quality measure influences the recommendation process and it is obtained by the combination of compatibility and similarity scores. In this paper, we describe new compatibility strategies whose basis is on reinforcement learning and a new feature weighting technique which is based on the user's history of critiques. Moreover, we show that our methodology can significantly improve recommendation efficiency in comparison with the state-of-the-art approaches.