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|Title:||Estimating Feature Ratings through an Effective Review Selection Approach|
FEATURE RATING ESTIMATION
|Citation:||Long, C., Zhang, J., Huang, X., Li, M., & Ma, B. (in press). Estimating feature ratings through an effective review selection approach. Knowledge and Information Systems.|
|Abstract:||Most participatory websites collect overall ratings (e.g. five stars) of products from their customers, reflecting the overall assessment of the products. However, it is more useful to present ratings of product features (such as price, battery, screen and lens of digital cameras) to help customers make effective purchase decisions. Unfortunately,only a very few websites have collected feature ratings. In this paper, we propose a novel approach to accurately estimate feature ratings of products. In this paper, we propose a novel approach to accurately estimate feature ratings of 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 both annotated and 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 can better help users make purchasing decisions.|
|Description:||Output of IDRC research project.|
Accepted and awaiting publication in the Journal of Knowledge and Information Systems.
|Project Title:||International Research Chairs Initiative (IRCI)|
|Access Restriction:||Due to copyright restrictions the full text of this research output is not available in the IDRC Digital Library or by request from the IDRC Library. / Compte tenu des restrictions relatives au droit d`auteur, le texte intégral de cet extrant de recherche n`est pas accessible dans la Bibliothèque numérique du CRDI, et il n`est pas possible d`en faire la demande à la Bibliothèque du CRDI.|
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