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Context-Aware Helpfulness Prediction for Online Product Reviews

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Information Retrieval Technology (AIRS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12004))

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Abstract

Modeling and prediction of review helpfulness has become more predominant due to proliferation of e-commerce websites and online shops. Since the functionality of a product cannot be tested before buying, people often rely on different kinds of user reviews to decide whether or not to buy a product. However, quality reviews might be buried deep in the heap of a large amount of reviews. Therefore, recommending reviews to customers based on the review quality is of the essence. Since there is no direct indication of review quality, most reviews use the information that “X out of Y” users found the review helpful for obtaining the review quality. However, this approach undermines helpfulness prediction because not all reviews have statistically abundant votes. In this paper, we propose a neural deep learning model that predicts the helpfulness score of a review. This model is based on convolutional neural network (CNN) and a context-aware encoding mechanism which can directly capture relationships between words irrespective of their distance in a long sequence. We validated our model on human annotated dataset and the result shows that our model significantly outperforms existing models for helpfulness prediction.

The work described in this paper is substantially supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14204418).

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Authors and Affiliations

  1. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong

    Iyiola E. Olatunji, Xin Li & Wai Lam

Authors
  1. Iyiola E. Olatunji
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  2. Xin Li
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  3. Wai Lam
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Corresponding author

Correspondence to Iyiola E. Olatunji .

Editor information

Editors and Affiliations

  1. Open University of Hong Kong, Hong Kong, China

    Fu Lee Wang

  2. The Education University of Hong Kong, Hong Kong, China

    Haoran Xie

  3. Chinese University of Hong Kong, Hong Kong, China

    Wai Lam

  4. Nanyang Technological University, Singapore, Singapore

    Aixin Sun

  5. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Lun-Wei Ku

  6. South China Normal University, Guangzhou, China

    Tianyong Hao

  7. Chinese Academy of Agricultural Sciences, Beijing, China

    Wei Chen

  8. Douglas College, New Westminster, BC, Canada

    Tak-Lam Wong

  9. University of Southern Queensland, Toowoomba, QLD, Australia

    Xiaohui Tao

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Olatunji, I.E., Li, X., Lam, W. (2020). Context-Aware Helpfulness Prediction for Online Product Reviews. In: Wang, F., et al. Information Retrieval Technology. AIRS 2019. Lecture Notes in Computer Science(), vol 12004. Springer, Cham. https://doi.org/10.1007/978-3-030-42835-8_6

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  • DOI: https://doi.org/10.1007/978-3-030-42835-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-42834-1

  • Online ISBN: 978-3-030-42835-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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