# MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendation

arXiv: https://arxiv.org/abs/2402.19407 · HTML: https://mlsanity.com/paper/2402.19407/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2024.

## Built on by (most cited first, 21 of 21)

- [Gated Multimodal Graph Learning for Personalized Recommendation](https://mlsanity.com/paper/2506.00107/) (2025)
- [AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation](https://mlsanity.com/paper/2409.02580/) (2024)
- [Spec-LLaVA: Accelerating Vision-Language Models with Dynamic Tree-Based Speculative Decoding](https://mlsanity.com/paper/2509.11961/) (2025)
- [GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation](https://mlsanity.com/paper/2407.12338/) (2024)
- [CPDR: Towards Highly-Efficient Salient Object Detection via Crossed Post-decoder Refinement](https://mlsanity.com/paper/2501.06441/) (2025)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation](https://mlsanity.com/paper/2512.01372/) (2025)
- [UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image Segmentation](https://mlsanity.com/paper/2501.06440/) (2025)
- [Learning and Editing Universal Graph Prompt Tuning via Reinforcement Learning](https://mlsanity.com/paper/2512.08763/) (2025)
- [Leveraging Optimal Transport for Distributed Two-Sample Testing: An Integrated Transportation Distance-based Framework](https://mlsanity.com/paper/2506.16047/) (2025)
- [TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization](https://mlsanity.com/paper/2503.03276/) (2025)
- [Segregation and Context Aggregation Network for Real-time Cloud Segmentation](https://mlsanity.com/paper/2504.14178/) (2025)
- [Causal Inspired Multi Modal Recommendation](https://mlsanity.com/paper/2510.12325/) (2025)
- [Enhancing Multimodal Recommendations with Vision-Language Models and Information-Aware Fusion](https://mlsanity.com/paper/2511.02113/) (2025)
- [Multi-modal Dynamic Proxy Learning for Personalized Multiple Clustering](https://mlsanity.com/paper/2511.07274/) (2025)
- [Modality-Guided Mixture of Structured Experts with Entropy-Triggered Routing for Multimodal Recommendation](https://mlsanity.com/paper/2602.20723/) (2026)
- [GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective](https://mlsanity.com/paper/2605.15723/) (2026)
- [F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting](https://mlsanity.com/paper/2608.09082/) (2026)
- [Agents as Knowledge Integrator and Utilizer in Multimodal Recommendation](https://mlsanity.com/paper/2608.29410/) (2026)
- [Beyond Modality Harmony: Orthogonal Purification and Topology-Guided MoE for Conflict-Aware Multimodal Recommendation](https://mlsanity.com/paper/2609.02152/) (2026)
- [Anatomy of a Decision: Uncertainty-aware Hierarchical Intent Learning via Flow Matching for Multimodal Recommendation](https://mlsanity.com/paper/2609.29609/) (2026)

## Abstract (arXiv)

With the increasing multimedia information, multimodal recommendation has received extensive attention. It utilizes multimodal information to alleviate the data sparsity problem in recommendation systems, thus improving recommendation accuracy. However, the reliance on labeled data severely limits the performance of multimodal recommendation models. Recently, self-supervised learning has been used in multimodal recommendations to mitigate the label sparsity problem. Nevertheless, the state-of-the-art methods cannot avoid the modality noise when aligning multimodal information due to the large differences in the distributions of different modalities. To this end, we propose a Multi-level sElf-supervised learNing for mulTimOdal Recommendation (MENTOR) method to address the label sparsity problem and the modality alignment problem. Specifically, MENTOR first enhances the specific features of each modality using the graph convolutional network (GCN) and fuses the visual and textual modalities. It then enhances the item representation via the item semantic graph for all modalities, including the fused modality. Then, it introduces two multilevel self-supervised tasks: the multilevel cross-modal alignment task and the general feature enhancement task. The multilevel cross-modal alignment task aligns each modality under the guidance of the ID embedding from multiple levels while maintaining the historical interaction information. The general feature enhancement task enhances the general feature from both the graph and feature perspectives to improve the robustness of our model. Extensive experiments on three publicly available datasets demonstrate the effectiveness of our method. Our code is publicly available at https://github.com/Jinfeng-Xu/MENTOR.