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Helpfulness Prediction for Online Reviews with Explicit Content-Rating Interaction

  • Jiahua Du
  • Jia RongEmail author
  • Hua Wang
  • Yanchun Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11881)

Abstract

Automatic helpfulness prediction aims to prioritize online product reviews by quality. Existing methods have combined review content and star ratings for automatic helpfulness prediction. However, the relationship between review content and star ratings is not explicitly captured, which limits the capability of rating information in influencing review content. This paper proposes a deep neural architecture to learn the explicit content-rating interaction (ECRI) for automatic helpfulness prediction. Specifically, ECRI explores two methods to interact review content with star ratings and adaptively specify the amount of rating information needed by review content. ECRI is evaluated against state-of-the-art methods on six real-world domains of the Amazon 5-core dataset. Experimental results demonstrate that exploiting the explicit content-rating interaction improves automatic helpfulness prediction. The source code of ECRI can be obtained from https://github.com/tokawah/ECRI.

Keywords

E-commerce Review helpfulness Explicit content-rating interaction Deep learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Sustainable Industries and Liveable CitiesVictoria UniversityMelbourneAustralia
  2. 2.Faculty of Information TechnologyMonash UniversityClaytonAustralia

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