TY - GEN
T1 - Quality enhancement based on reinforcement learning and feature weighting for a critiquing-based recommender
AU - Salamó, Maria
AU - Escalera, Sergio
AU - Radeva, Petia
PY - 2009
Y1 - 2009
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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70350356624&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02998-1_22
DO - 10.1007/978-3-642-02998-1_22
M3 - Article in proceeding
AN - SCOPUS:70350356624
SN - 3642029973
SN - 9783642029974
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 298
EP - 312
BT - Case-Based Reasoning Research and Development - 8th International Conference on Case-Based Reasoning, ICCBR 2009, Proceedings
T2 - 8th International Conference on Case-Based Reasoning, ICCBR 2009
Y2 - 20 July 2009 through 23 July 2009
ER -