mlsanityData to 7 Oct 2026

SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning

arXiv 2209.05040 ↗First posted 12 Sep 2022cs.CL cs.AIcited by 12

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Abstract (arXiv)

With the boom of e-commerce, Multimodal Review Helpfulness Prediction (MRHP), which aims to sort product reviews according to the predicted helpfulness scores has become a research hotspot. Previous work on this task focuses on attention-based modality fusion, information integration, and relation modeling, which primarily exposes the following drawbacks: 1) the model may fail to capture the really essential information due to its indiscriminate attention formulation; 2) lack appropriate modeling methods that take full advantage of correlation among provided data. In this paper, we propose SANCL: Selective Attention and Natural Contrastive Learning for MRHP. SANCL adopts a probe-based strategy to enforce high attention weights on the regions of greater significance. It also constructs a contrastive learning framework based on natural matching properties in the dataset. Experimental results on two benchmark datasets with three categories show that SANCL achieves state-of-the-art baseline performance with lower memory consumption.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 19 of its 50 references that are in the map. First posted on arXiv 12 Sep 2022.

Builds on

19 of 50 references.

References · 19

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7 more
  1. 2019Modality to Modality Translation: An Adversarial Representation Learning and Graph Fusion Network for Multimodal Fusioncited by 45
  2. 2021Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysiscited by 45
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  5. 2021Identifying Helpful Sentences in Product Reviewscited by 9
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 2; 2025: 2; 2026: 2.

12 papers.

  1. 2024Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyondcited by 25
  2. 2022Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictionscited by 13
  3. 2023Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learningcited by 8
  4. 2023Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Predictioncited by 7
  5. 2022Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learningcited by 6
  6. 2023On the Role of Reviewer Expertise in Temporal Review Helpfulness Predictioncited by 6
  7. 2023One-Versus-Others Attention: Scalable Multimodal Integration for Biomedical Datacited by 6
  8. 2024Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodationscited by 2
  9. 2026EffiMiniVLM: A Compact Dual-Encoder Regression Frameworkcited by 1
  10. 2026Bounded-Compute Multimodal Regression for Product-Rating Predictioncited by 1
  11. 2025PREMISE: Matching-based Prediction for Accurate Review Recommendationcited by 0
  12. 2025ViMRHP: A Vietnamese Benchmark Dataset for Multimodal Review Helpfulness Prediction via Human-AI Collaborative Annotationcited by 0