# Bootstrap Latent Representations for Multi-modal Recommendation

arXiv: https://arxiv.org/abs/2207.05969 · HTML: https://mlsanity.com/paper/2207.05969/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2022.

## Built on by (most cited first, 30 of 55)

- [Multimodal Recommender Systems: A Survey](https://mlsanity.com/paper/2302.03883/) (2023)
- [MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation](https://mlsanity.com/paper/2308.11175/) (2023)
- [DiffMM: Multi-Modal Diffusion Model for Recommendation](https://mlsanity.com/paper/2406.11781/) (2024)
- [AlignRec: Aligning and Training in Multimodal Recommendations](https://mlsanity.com/paper/2403.12384/) (2024)
- [Rec-GPT4V: Multimodal Recommendation with Large Vision-Language Models](https://mlsanity.com/paper/2402.08670/) (2024)
- [LD4MRec: Simplifying and Powering Diffusion Model for Multimedia Recommendation](https://mlsanity.com/paper/2309.15363/) (2023)
- [GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation](https://mlsanity.com/paper/2407.12338/) (2024)
- [Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima](https://mlsanity.com/paper/2402.11262/) (2024)
- [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)
- [MUSE: A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling](https://mlsanity.com/paper/2512.07216/) (2025)
- [Inductive Graph Transformer for Delivery Time Estimation](https://mlsanity.com/paper/2211.02863/) (2022)
- [Multi-Modal Recommendation Unlearning for Legal, Licensing, and Modality Constraints](https://mlsanity.com/paper/2405.15328/) (2024)
- [Modality-Balanced Learning for Multimedia Recommendation](https://mlsanity.com/paper/2408.06360/) (2024)
- [LinkedOut: Linking World Knowledge Representation Out of Video LLM for Next-Generation Video Recommendation](https://mlsanity.com/paper/2512.16891/) (2025)
- [A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation](https://mlsanity.com/paper/2407.19886/) (2024)
- [Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios](https://mlsanity.com/paper/2504.16352/) (2025)
- [RecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment](https://mlsanity.com/paper/2602.00682/) (2026)
- [ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation](https://mlsanity.com/paper/2311.05956/) (2023)
- [Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation](https://mlsanity.com/paper/2508.04571/) (2025)
- [Dual-Phase Playtime-guided Recommendation: Interest Intensity Exploration and Multimodal Random Walks](https://mlsanity.com/paper/2508.14058/) (2025)
- [COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation](https://mlsanity.com/paper/2306.05001/) (2023)
- [Training-free Adjustable Polynomial Graph Filtering for Ultra-fast Multimodal Recommendation](https://mlsanity.com/paper/2503.04406/) (2025)
- [MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2504.16576/) (2025)
- [Federated Vision-Language-Recommendation with Personalized Fusion](https://mlsanity.com/paper/2410.08478/) (2024)
- [A Metric for MLLM Alignment in Large-scale Recommendation](https://mlsanity.com/paper/2508.04963/) (2025)
- [MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation](https://mlsanity.com/paper/2508.14515/) (2025)
- [Rejuvenating Cross-Entropy Loss in Knowledge Distillation for Recommender Systems](https://mlsanity.com/paper/2509.20989/) (2025)
- [Benchmarking Multimodal Large Language Models for Missing Modality Completion in Product Catalogues](https://mlsanity.com/paper/2601.19750/) (2026)
- [BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models](https://mlsanity.com/paper/2601.22925/) (2026)

## Abstract (arXiv)

This paper studies the multi-modal recommendation problem, where the item multi-modality information (e.g., images and textual descriptions) is exploited to improve the recommendation accuracy. Besides the user-item interaction graph, existing state-of-the-art methods usually use auxiliary graphs (e.g., user-user or item-item relation graph) to augment the learned representations of users and/or items. These representations are often propagated and aggregated on auxiliary graphs using graph convolutional networks, which can be prohibitively expensive in computation and memory, especially for large graphs. Moreover, existing multi-modal recommendation methods usually leverage randomly sampled negative examples in Bayesian Personalized Ranking (BPR) loss to guide the learning of user/item representations, which increases the computational cost on large graphs and may also bring noisy supervision signals into the training process. To tackle the above issues, we propose a novel self-supervised multi-modal recommendation model, dubbed BM3, which requires neither augmentations from auxiliary graphs nor negative samples. Specifically, BM3 first bootstraps latent contrastive views from the representations of users and items with a simple dropout augmentation. It then jointly optimizes three multi-modal objectives to learn the representations of users and items by reconstructing the user-item interaction graph and aligning modality features under both inter- and intra-modality perspectives. BM3 alleviates both the need for contrasting with negative examples and the complex graph augmentation from an additional target network for contrastive view generation. We show BM3 outperforms prior recommendation models on three datasets with number of nodes ranging from 20K to 200K, while achieving a 2-9X reduction in training time. Our code is available at https://github.com/enoche/BM3.