TY  - CONF
AU  - Maria Salamo
AU  - Sergio Escalera
AU  - Petia Radeva
A2  - ICCBR
PY  - 2009//
TI  - Quality Enhancement based on Reinforcement Learning and Feature Weighting for a Critiquing-Based Recommender
T2  - LNCS
BT  - 8th International Conference on Case-Based Reasoning
SP  - 298–312
VL  - 5650
PB  - Springer Berlin Heidelberg
N2  - 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.
SN  - 0302-9743
SN  - 978-3-642-02998-1
UR  - http://dx.doi.org/10.1007/978-3-642-02998-1_22
N1  - HuPBA; MILAB
ID  - Maria Salamo2009
ER  -