Specialized Review Selection for Feature Rating Estimation

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Please use this identifier to cite or link to this item: http://hdl.handle.net/10625/49063

Title:	Specialized Review Selection for Feature Rating Estimation
Authors:	Long, Chong Zhang, Lei Huang, Minlie Zhu, Xiaoyan Li, Ming
Keywords:	DATA MINING TEXT MINING KOLMOGOROV COMPLEXITY INFORMATION DISTANCE
Issue Date:	Sep-2009
Publisher:	IEEE
Citation:	Long, C., Zhang, L., Huang, M., Zhu, X., & Li, M. (2009). Specialized Review Selection for Feature Rating Estimation. Proceedings of the 2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology – Workshops. (p.214-221). IEEE. doi:10.1109/WI-IAT.2009.38
Abstract:	On participatory Websites, users provide opinions about products, with both overall ratings and textual reviews. In this paper, we propose an approach to accurately estimate feature ratings of the products. This approach selects user reviews that extensively discuss specific features of the products (called specialized reviews), using information distance of reviews on the features. Experiments on real data show that overall ratings of the specialized reviews can be used to represent their feature ratings. The average of these overall ratings can be used by recommender systems to provide feature specific recommendations that better help users make purchasing decisions.
Description:	2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops
URI:	http://hdl.handle.net/10625/49063
ISBN:	978-0-7695-3801-3
Project Number:	104519
Project Title:	International Research Chairs Initiative (IRCI)
Document Delivery:	This document is not available in the IDRC Digital Library / Ce document n'est pas disponible dans la Bibliothèque numérique du CRDI
Appears in Collections:	2000-2009 / Années 2000-2009 Breaking the barriers to Internet access / Faire tomber les obstacles entravant l’accès à Internet IDRC Research Results / Résultats de recherches du CRDI

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