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ICCBR
2009
Springer

Quality Enhancement Based on Reinforcement Learning and Feature Weighting for a Critiquing-Based Recommender

9 years 3 months ago
Quality Enhancement Based on Reinforcement Learning and Feature Weighting for a Critiquing-Based Recommender
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.
Maria Salamó, Sergio Escalera, Petia Radeva
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ICCBR
Authors Maria Salamó, Sergio Escalera, Petia Radeva
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