# A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions

arXiv: https://arxiv.org/abs/2302.04473 · HTML: https://mlsanity.com/paper/2302.04473/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2023.

## Built on by (most cited first, 17 of 17)

- [MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation](https://mlsanity.com/paper/2308.11175/) (2023)
- [Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case Study](https://mlsanity.com/paper/2311.04199/) (2023)
- [Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large-Scale Recommendation](https://mlsanity.com/paper/2403.00877/) (2024)
- [Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation](https://mlsanity.com/paper/2512.01372/) (2025)
- [Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudging](https://mlsanity.com/paper/2309.01026/) (2023)
- [Pareto Invariant Representation Learning for Multimedia Recommendation](https://mlsanity.com/paper/2308.04706/) (2023)
- [CADMR: Cross-Attention and Disentangled Learning for Multimodal Recommender Systems](https://mlsanity.com/paper/2412.02295/) (2024)
- [Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation](https://mlsanity.com/paper/2407.18937/) (2024)
- [Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation](https://mlsanity.com/paper/2412.13994/) (2024)
- [MDE: Modality Discrimination Enhancement for Multi-modal Recommendation](https://mlsanity.com/paper/2502.18481/) (2025)
- [Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR Challenge](https://mlsanity.com/paper/2504.18961/) (2025)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)
- [Cross-Modal Attention Network with Dual Graph Learning in Multimodal Recommendation](https://mlsanity.com/paper/2601.11151/) (2026)
- [DReX: An Explainable Deep Learning-based Multimodal Recommendation Framework](https://mlsanity.com/paper/2602.19702/) (2026)
- [E-MMKGR: A Unified Multimodal Knowledge Graph Framework for E-commerce Applications](https://mlsanity.com/paper/2602.20877/) (2026)
- [TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning](https://mlsanity.com/paper/2604.02183/) (2026)
- [Band Together: Untargeted Adversarial Training with Multimodal Coordination against Evasion-based Promotion Attacks](https://mlsanity.com/paper/2605.06238/) (2026)

## Abstract (arXiv)

Recommendation systems have become popular and effective tools to help users discover their interesting items by modeling the user preference and item property based on implicit interactions (e.g., purchasing and clicking). Humans perceive the world by processing the modality signals (e.g., audio, text and image), which inspired researchers to build a recommender system that can understand and interpret data from different modalities. Those models could capture the hidden relations between different modalities and possibly recover the complementary information which can not be captured by a uni-modal approach and implicit interactions. The goal of this survey is to provide a comprehensive review of the recent research efforts on the multimodal recommendation. Specifically, it shows a clear pipeline with commonly used techniques in each step and classifies the models by the methods used. Additionally, a code framework has been designed that helps researchers new in this area to understand the principles and techniques, and easily runs the SOTA models. Our framework is located at: https://github.com/enoche/MMRec