# DiffCL: A Diffusion-Based Contrastive Learning Framework with Semantic Alignment for Multimodal Recommendations

arXiv: https://arxiv.org/abs/2501.01066 · HTML: https://mlsanity.com/paper/2501.01066/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2025.

## Built on by (most cited first, 9 of 9)

- [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025)
- [Occlusion-Aware 3D Hand-Object Pose Estimation with Masked AutoEncoders](https://mlsanity.com/paper/2506.10816/) (2025)
- [Diffusion-augmented Graph Contrastive Learning for Collaborative Filter](https://mlsanity.com/paper/2503.16290/) (2025)
- [Manifold-Aware Diffusion-Augmented Contrastive Learning for Noise-Robust Biosignal Representation](https://mlsanity.com/paper/2509.20048/) (2025)
- [Multimodal Representation-disentangled Information Bottleneck for Multimodal Recommendation](https://mlsanity.com/paper/2509.20225/) (2025)
- [MGDiff: Multi-Interest Sequence Recommendation with Masking GNN-Guided Diffusion](https://mlsanity.com/paper/2609.01619/) (2026)
- [MURAL: Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning](https://mlsanity.com/paper/2609.04574/) (2026)
- [Anatomy of a Decision: Uncertainty-aware Hierarchical Intent Learning via Flow Matching for Multimodal Recommendation](https://mlsanity.com/paper/2609.29609/) (2026)
- [FairDiff: Mitigating the Self-Reinforcing Matthew Effect in Diffusion Recommender Models](https://mlsanity.com/paper/2609.36671/) (2026)

## Abstract (arXiv)

Multimodal recommendation systems integrate diverse multimodal information into the feature representations of both items and users, thereby enabling a more comprehensive modeling of user preferences. However, existing methods are hindered by data sparsity and the inherent noise within multimodal data, which impedes the accurate capture of users' interest preferences. Additionally, discrepancies in the semantic representations of items across different modalities can adversely impact the prediction accuracy of recommendation models. To address these challenges, we introduce a novel diffusion-based contrastive learning framework (DiffCL) for multimodal recommendation. DiffCL employs a diffusion model to generate contrastive views that effectively mitigate the impact of noise during the contrastive learning phase. Furthermore, it improves semantic consistency across modalities by aligning distinct visual and textual semantic information through stable ID embeddings. Finally, the introduction of the Item-Item Graph enhances multimodal feature representations, thereby alleviating the adverse effects of data sparsity on the overall system performance. We conduct extensive experiments on three public datasets, and the results demonstrate the superiority and effectiveness of the DiffCL.