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    <abstract>Helpful reviews have been essential for the success of e-commerce services, as they help customers make quick purchase decisions and benefit the merchants in their sales. While many reviews are informative, others provide little value and may contain spam, excessive appraisal, or unexpected biases. With the large volume of reviews and their uneven quality, the problem of detecting helpful reviews has drawn much attention lately. Existing methods for identifying helpful reviews primarily focus on review text and ignore the two key factors of (1) who post the reviews and (2) when the reviews are posted. Moreover, the helpfulness votes suffer from scarcity for less popular products and recently submitted (a.k.a., cold-start) reviews. To address these challenges, we introduce a dataset and develop a model that integrates the reviewer’s expertise, derived from the past review history of the reviewers, and the temporal dynamics of the reviews to automatically assess review helpfulness. We conduct experiments on our dataset to demonstrate the effectiveness of incorporating these factors and report improved results compared to several well-established baselines.</abstract>
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%0 Conference Proceedings
%T On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction
%A Nayeem, Mir Tafseer
%A Rafiei, Davood
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F nayeem-rafiei-2023-role
%X Helpful reviews have been essential for the success of e-commerce services, as they help customers make quick purchase decisions and benefit the merchants in their sales. While many reviews are informative, others provide little value and may contain spam, excessive appraisal, or unexpected biases. With the large volume of reviews and their uneven quality, the problem of detecting helpful reviews has drawn much attention lately. Existing methods for identifying helpful reviews primarily focus on review text and ignore the two key factors of (1) who post the reviews and (2) when the reviews are posted. Moreover, the helpfulness votes suffer from scarcity for less popular products and recently submitted (a.k.a., cold-start) reviews. To address these challenges, we introduce a dataset and develop a model that integrates the reviewer’s expertise, derived from the past review history of the reviewers, and the temporal dynamics of the reviews to automatically assess review helpfulness. We conduct experiments on our dataset to demonstrate the effectiveness of incorporating these factors and report improved results compared to several well-established baselines.
%R 10.18653/v1/2023.findings-eacl.125
%U https://aclanthology.org/2023.findings-eacl.125
%U https://doi.org/10.18653/v1/2023.findings-eacl.125
%P 1684-1692

Markdown (Informal)

[On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction](https://aclanthology.org/2023.findings-eacl.125) (Nayeem & Rafiei, Findings 2023)

• On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction (Nayeem & Rafiei, Findings 2023)

ACL

• Mir Tafseer Nayeem and Davood Rafiei. 2023. On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1684–1692, Dubrovnik, Croatia. Association for Computational Linguistics.