title: ViMRHP: A Vietnamese Benchmark Dataset for Multimodal Review Helpfulness
    Prediction via Human-AI Collaborative Annotation
summary: Multimodal Review Helpfulness Prediction (MRHP) is an essential task in
  recommender systems, particularly in E-commerce platforms. Determining the
  helpfulness of user-generated reviews enhances user experience and improves
  consumer decision-making. However, existing datasets focus predominantly on
  English and Indonesian, resulting in a lack of linguistic diversity, especially
  for low-resource languages such as Vietnamese. In this paper, we introduce
  ViMRHP (Vietnamese Multimodal Review Helpfulness Prediction), a large-scale
  benchmark dataset for MRHP task in Vietnamese. This dataset covers four
  domains, including 2K products with 46K reviews. Meanwhile, a large-scale
  dataset requires considerable time and cost. To optimize the annotation
  process, we leverage AI to assist annotators in constructing the ViMRHP
  dataset. With AI assistance, annotation time is reduced (90 to 120 seconds per
  task down to 20 to 40 seconds per task) while maintaining data quality and
  lowering overall costs by approximately 65%. However, AI-generated annotations
  still have limitations in complex annotation tasks, which we further examine
  through a detailed performance analysis. In our experiment on ViMRHP, we
  evaluate baseline models on human-verified and AI-generated annotations to
  assess their quality differences. The ViMRHP dataset is publicly available at
  https://github.com/trng28/ViMRHP
paper_authors._id: 68238a5124c55c2bd5bec8b5
paper_authors.name: Truc Mai-Thanh Nguyen
paper_authors._id: 68238a5124c55c2bd5bec8b6
paper_authors.name: Dat Minh Nguyen
paper_authors._id: 68238a5124c55c2bd5bec8b7
paper_authors.name: Son T. Luu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T10:13:35.878Z
paper_authors._id: 60bb728e29800c34660339e3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/60bb728e29800c34660339e3/kIscETb7-lF5u2jHOJ-dR.png
paper_authors.fullname: Son T. Luu 
paper_authors.type: user
paper_authors.user: sonlam1102
paper_authors._id: 68238a5124c55c2bd5bec8b8
paper_authors.name: Kiet Van Nguyen
paper_title: ViMRHP: A Vietnamese Benchmark Dataset for Multimodal Review Helpfulness
    Prediction via Human-AI Collaborative Annotation
paper_summary: Multimodal Review Helpfulness Prediction (MRHP) is an essential task in
  recommender systems, particularly in E-commerce platforms. Determining the
  helpfulness of user-generated reviews enhances user experience and improves
  consumer decision-making. However, existing datasets focus predominantly on
  English and Indonesian, resulting in a lack of linguistic diversity, especially
  for low-resource languages such as Vietnamese. In this paper, we introduce
  ViMRHP (Vietnamese Multimodal Review Helpfulness Prediction), a large-scale
  benchmark dataset for MRHP task in Vietnamese. This dataset covers four
  domains, including 2K products with 46K reviews. Meanwhile, a large-scale
  dataset requires considerable time and cost. To optimize the annotation
  process, we leverage AI to assist annotators in constructing the ViMRHP
  dataset. With AI assistance, annotation time is reduced (90 to 120 seconds per
  task down to 20 to 40 seconds per task) while maintaining data quality and
  lowering overall costs by approximately 65%. However, AI-generated annotations
  still have limitations in complex annotation tasks, which we further examine
  through a detailed performance analysis. In our experiment on ViMRHP, we
  evaluate baseline models on human-verified and AI-generated annotations to
  assess their quality differences. The ViMRHP dataset is publicly available at
  https://github.com/trng28/ViMRHP
paper_ai_summary: A Vietnamese multimodal review helpfulness prediction dataset (ViMRHP) is introduced, optimized using AI for annotation, and evaluated for performance in comparison to human-verified data.
paper_ai_keywords: multimodal review helpfulness prediction recommender systems Vietnamese large-scale benchmark dataset AI assistance annotation optimization performance analysis baseline models