ViMRHP: A Vietnamese Benchmark Dataset for Multimodal Review Helpfulness
  Prediction via Human-AI Collaborative Annotation
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
68238a5124c55c2bd5bec8b5
Truc Mai-Thanh Nguyen
claimed_verified
2025-07-08T09:05:54.572Z
68238deb87e1ba084e455129
https://cdn-avatars.huggingface.co/v1/production/uploads/68238deb87e1ba084e455129/PJ8OOODIDErGe7PokOnRP.jpeg
Truc Nguyen
user
trucmtnguyen
68238a5124c55c2bd5bec8b6
Dat Minh Nguyen
68238a5124c55c2bd5bec8b7
Son T. Luu
admin_assigned
2025-05-14T10:13:35.878Z
60bb728e29800c34660339e3
https://cdn-avatars.huggingface.co/v1/production/uploads/60bb728e29800c34660339e3/kIscETb7-lF5u2jHOJ-dR.png
Son T. Luu 
user
sonlam1102
68238a5124c55c2bd5bec8b8
Kiet Van Nguyen
claimed_verified
2025-06-09T10:13:15.034Z
60b86a2d111df7ee2053d54e
/avatars/8eb24d497b4eb0ba86edc2c4b41b2524.svg
Kiet Nguyen
user
kietnv
ViMRHP: A Vietnamese Benchmark Dataset for Multimodal Review Helpfulness
  Prediction via Human-AI Collaborative Annotation
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
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.
multimodal review helpfulness prediction recommender systems Vietnamese large-scale benchmark dataset AI assistance annotation optimization performance analysis baseline models