Estimating feature ratings through an effective review selection approach
Knowledge and Information SystemsPublished 4 October 2013
Chong Long, Jie Zhang, Minlie Huang, Xiaoyan Zhu, Ming Li, Bin Ma
Citations13
SJR quartileQ2
SJR score0.83
SNIP1.37
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TL;DR
This paper proposes a novel approach to accurately estimate feature ratings of products, and selects user reviews that extensively discuss specific features of the products (called specialized reviews), using information distance of reviews on the features.
Abstract
Most participatory web sites 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 web sites have collected feature ratings. 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.
Keywords
Computer ScienceBusiness, Management and Accounting
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