mlsanityData to 7 Oct 2026

On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction

arXiv 2303.00923 ↗First posted 22 Feb 2023cs.CL cs.IRcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 11 of its 37 references that are in the map. First posted on arXiv 22 Feb 2023.

Builds on

11 of 37 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
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  8. 2019BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysiscited by 176
  9. 2022SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learningcited by 12
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  11. 2017Using Argument-based Features to Predict and Analyse Review Helpfulnesscited by 6

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Cited by year

Citing papers per year: 2024: 3; 2025: 3.

6 papers.

  1. 2024KidLM: Advancing Language Models for Children -- Early Insights and Future Directionscited by 7
  2. 2025OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviewscited by 3
  3. 2024Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodationscited by 2
  4. 2025eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tablescited by 2
  5. 2024LFOSum: Summarizing Long-form Opinions with Large Language Modelscited by 1
  6. 2025ViMRHP: A Vietnamese Benchmark Dataset for Multimodal Review Helpfulness Prediction via Human-AI Collaborative Annotationcited by 0