Abstract
Review helpfulness prediction aims to prioritize online reviews by quality. Existing methods largely combine review texts and star ratings for helpfulness prediction. However, star ratings are used in a way that has either little representation capacity or limited interaction with review texts. As a result, rating information has yet to be fully exploited during the combination. This paper aims to overcome the two drawbacks. A deep interactive architecture is proposed to learn the text–rating interaction (TRI) for helpfulness modeling. TRI enlarges the representation capacity of star ratings while enhancing the influence of rating information on review texts. TRI is evaluated on six real-world domains of the Amazon 5-Core dataset. Extensive experiments demonstrate that TRI can better predict review helpfulness and beat the state of the art. Ablation studies and qualitative analysis are provided to further understand model behaviors and the learned parameters.
Highlights
Online reviews play an important role in the e-commerce ecosystem
This paper has presented text–rating interaction (TRI), a deep neural architecture that learns the interaction between review texts and star ratings for helpfulness prediction
In contrast to prior work that underdevelops rating information, TRI originally (1) enlarged the encoding space of star ratings, (2) allowed for adaptive rating information learning, and (3) maintained the influence of star ratings when interacting with review texts
Summary
Online reviews play an important role in the e-commerce ecosystem. Online buyers highly rely on collective wisdom to make informed purchase decisions. A recent survey [43] shows that over 8 of 10 customers read reviews. Helpfulness prediction aims to identify and recommend high-quality reviews to customers in an automatic manner. The previous literature [2, 22, 44] largely employs review texts and star ratings for the task. The rationale lies in their ubiquitousness in contemporary online shopping platforms and their importance to review helpfulness modeling. Review texts qualitatively describe reviewers’ opinions toward product properties. Star ratings [40] provide a more straightforward form to quantify reviewers’ opinions. The valence (positive or negative) [66] and extremity [15, 45, 57] of ratings are shown to have considerable impact on review helpfulness
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