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

arXiv: https://arxiv.org/abs/2209.05040 · HTML: https://mlsanity.com/paper/2209.05040/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 12 Sep 2022.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2109.00412/) (2021)
- [Multimodal Contrastive Training for Visual Representation Learning](https://mlsanity.com/paper/2104.12836/) (2021)
- [Bilateral Multi-Perspective Matching for Natural Language Sentences](https://mlsanity.com/paper/1702.03814/) (2017)
- [Deep Multimodal Fusion by Channel Exchanging](https://mlsanity.com/paper/2011.05005/) (2020)
- [CentralNet: a Multilayer Approach for Multimodal Fusion](https://mlsanity.com/paper/1808.07275/) (2018)
- [Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research](https://mlsanity.com/paper/2005.00357/) (2020)
- [Modality to Modality Translation: An Adversarial Representation Learning and Graph Fusion Network for Multimodal Fusion](https://mlsanity.com/paper/1911.07848/) (2019)
- [Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis](https://mlsanity.com/paper/2107.13669/) (2021)
- [Multimodal Categorization of Crisis Events in Social Media](https://mlsanity.com/paper/2004.04917/) (2020)
- [Analyzing Modality Robustness in Multimodal Sentiment Analysis](https://mlsanity.com/paper/2205.15465/) (2022)
- [Identifying Helpful Sentences in Product Reviews](https://mlsanity.com/paper/2104.09792/) (2021)
- [Review-based Question Generation with Adaptive Instance Transfer and Augmentation](https://mlsanity.com/paper/1911.01556/) (2019)
- [Unsupervised Natural Language Inference via Decoupled Multimodal Contrastive Learning](https://mlsanity.com/paper/2010.08200/) (2020)

## Built on by (most cited first, 12 of 12)

- [Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond](https://mlsanity.com/paper/2403.10667/) (2024)
- [Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions](https://mlsanity.com/paper/2211.03524/) (2022)
- [Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning](https://mlsanity.com/paper/2305.13628/) (2023)
- [Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction](https://mlsanity.com/paper/2305.12678/) (2023)
- [Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning](https://mlsanity.com/paper/2210.07715/) (2022)
- [On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction](https://mlsanity.com/paper/2303.00923/) (2023)
- [One-Versus-Others Attention: Scalable Multimodal Integration for Biomedical Data](https://mlsanity.com/paper/2307.05435/) (2023)
- [Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodations](https://mlsanity.com/paper/2407.00787/) (2024)
- [EffiMiniVLM: A Compact Dual-Encoder Regression Framework](https://mlsanity.com/paper/2604.03172/) (2026)
- [Bounded-Compute Multimodal Regression for Product-Rating Prediction](https://mlsanity.com/paper/2605.27737/) (2026)
- [PREMISE: Matching-based Prediction for Accurate Review Recommendation](https://mlsanity.com/paper/2505.01255/) (2025)
- [ViMRHP: A Vietnamese Benchmark Dataset for Multimodal Review Helpfulness Prediction via Human-AI Collaborative Annotation](https://mlsanity.com/paper/2505.07416/) (2025)

## 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.