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Estimating feature ratings through an effective review selection approach

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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.

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Author information

Authors and Affiliations

  1. Yahoo! Labs, Beijing, China

    Chong Long

  2. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Jie Zhang

  3. State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, 100084, China

    Minlie Huang & Xiaoyan Zhu

  4. School of Computer Science, University of Waterloo, Waterloo, Canada

    Ming Li & Bin Ma

Authors
  1. Chong Long
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  2. Jie Zhang
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  3. Minlie Huang
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  4. Xiaoyan Zhu
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  5. Ming Li
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  6. Bin Ma
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Corresponding author

Correspondence to Xiaoyan Zhu.

Additional information

This work was done when C. Long was a Ph.D. student in Tsinghua University.

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Long, C., Zhang, J., Huang, M. et al. Estimating feature ratings through an effective review selection approach. Knowl Inf Syst 38, 419–446 (2014). https://doi.org/10.1007/s10115-012-0495-8

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  • DOI: https://doi.org/10.1007/s10115-012-0495-8

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