# Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback

arXiv: https://arxiv.org/abs/2406.12501 · HTML: https://mlsanity.com/paper/2406.12501/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2024.

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

- [Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation](https://mlsanity.com/paper/2512.01372/) (2025)
- [Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios](https://mlsanity.com/paper/2504.16352/) (2025)
- [Semantic Item Graph Enhancement for Multimodal Recommendation](https://mlsanity.com/paper/2508.06154/) (2025)
- [Token-Efficient Item Representation via Images for LLM Recommender Systems](https://mlsanity.com/paper/2503.06238/) (2025)
- [Improving LLM Interpretability and Performance via Guided Embedding Refinement for Sequential Recommendation](https://mlsanity.com/paper/2504.11658/) (2025)
- [From Entity Reliability to Clean Feedback: An Entity-Aware Denoising Framework Beyond Interaction-Level Signals](https://mlsanity.com/paper/2508.10851/) (2025)
- [EGRA:Toward Enhanced Behavior Graphs and Representation Alignment for Multimodal Recommendation](https://mlsanity.com/paper/2508.16170/) (2025)
- [Causal Inspired Multi Modal Recommendation](https://mlsanity.com/paper/2510.12325/) (2025)
- [Anchored Alignment: Preventing Positional Collapse in Multimodal Recommender Systems](https://mlsanity.com/paper/2603.12726/) (2026)
- [MedicalRec: Medical recommender system for image classification without retraining](https://mlsanity.com/paper/2606.07553/) (2026)
- [Beyond Modality Harmony: Orthogonal Purification and Topology-Guided MoE for Conflict-Aware Multimodal Recommendation](https://mlsanity.com/paper/2609.02152/) (2026)
- [OT-FlowRec: Optimal Transport-Guided Multi-Bridge Flow for Multimodal Sequential Recommendation](https://mlsanity.com/paper/2610.16038/) (2026)

## Abstract (arXiv)

Multi-modal recommender systems (MRSs) are pivotal in diverse online web platforms and have garnered considerable attention in recent years. However, previous studies overlook the challenges of (1) noisy multi-modal content, (2) noisy user feedback, and (3) aligning multi-modal content with user feedback. In order to tackle these challenges, we propose Denoising and Aligning Multi-modal Recommender System (DA-MRS). To mitigate multi-modal noise, DA-MRS first constructs item-item graphs determined by consistent content similarity across modalities. To denoise user feedback, DA-MRS associates the probability of observed feedback with multi-modal content and devises a denoised BPR loss. Furthermore, DA-MRS implements Alignment guided by User preference to enhance task-specific item representation and Alignment guided by graded Item relations to provide finer-grained alignment. Extensive experiments verify that DA-MRS is a plug-and-play framework and achieves significant and consistent improvements across various datasets, backbone models, and noisy scenarios.