Specialized Review Selection for Feature Rating Estimation
Date
2009-09
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
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
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Conference Paper
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Text
Keywords
DATA MINING, TEXT MINING, KOLMOGOROV COMPLEXITY, INFORMATION DISTANCE, PRODUCT REVIEWS, RANKING, FEATURE EXTRACTION
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