# Multimodal Recommender Systems: A Survey

arXiv: https://arxiv.org/abs/2302.03883 · HTML: https://mlsanity.com/paper/2302.03883/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 95 references that are in the map.
- First posted on arXiv 8 Feb 2023.

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2112.08679/) (2021)
- [Learning Disentangled Representations for Recommendation](https://mlsanity.com/paper/1910.14238/) (2019)
- [Mining Latent Structures for Multimedia Recommendation](https://mlsanity.com/paper/2104.09036/) (2021)
- [Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation](https://mlsanity.com/paper/1901.08907/) (2019)
- [POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion](https://mlsanity.com/paper/1905.01866/) (2019)
- [GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback](https://mlsanity.com/paper/2111.02036/) (2021)
- [Bootstrap Latent Representations for Multi-modal Recommendation](https://mlsanity.com/paper/2207.05969/) (2022)
- [A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation](https://mlsanity.com/paper/2211.06924/) (2022)
- [Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion](https://mlsanity.com/paper/2205.02357/) (2022)
- [Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation](https://mlsanity.com/paper/2103.03578/) (2021)
- [TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback](https://mlsanity.com/paper/2206.06190/) (2022)
- [CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation](https://mlsanity.com/paper/2206.00242/) (2022)
- [A Review of Modern Fashion Recommender Systems](https://mlsanity.com/paper/2202.02757/) (2022)
- [Bundle Recommendation with Graph Convolutional Networks](https://mlsanity.com/paper/2005.03475/) (2020)
- [Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training](https://mlsanity.com/paper/2104.09411/) (2021)
- [Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation](https://mlsanity.com/paper/2111.00678/) (2021)
- [Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach](https://mlsanity.com/paper/1905.12862/) (2019)
- [Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation](https://mlsanity.com/paper/2005.10473/) (2020)
- [MMRec: Simplifying Multimodal Recommendation](https://mlsanity.com/paper/2302.03497/) (2023)
- [Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation](https://mlsanity.com/paper/1806.08977/) (2018)
- [Disentangled Multimodal Representation Learning for Recommendation](https://mlsanity.com/paper/2203.05406/) (2022)
- [Content-driven Music Recommendation: Evolution, State of the Art, and Challenges](https://mlsanity.com/paper/2107.11803/) (2021)

## Built on by (most cited first, 30 of 37)

- [A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge](https://mlsanity.com/paper/2310.11703/) (2023)
- [LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation](https://mlsanity.com/paper/2405.20646/) (2024)
- [A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval](https://mlsanity.com/paper/2503.05659/) (2025)
- [Language Representations Can be What Recommenders Need: Findings and Potentials](https://mlsanity.com/paper/2407.05441/) (2024)
- [Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2504.18383/) (2025)
- [Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case Study](https://mlsanity.com/paper/2311.04199/) (2023)
- [Hyperbolic Diffusion Recommender Model](https://mlsanity.com/paper/2504.01541/) (2025)
- [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)
- [Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning](https://mlsanity.com/paper/2506.05826/) (2025)
- [Inference-Time Dynamic Modality Selection for Incomplete Multimodal Classification](https://mlsanity.com/paper/2601.22853/) (2026)
- [GPRec: Bi-level User Modeling for Deep Recommenders](https://mlsanity.com/paper/2410.20730/) (2024)
- [AgentSelect: Benchmark for Narrative Query-to-Agent Recommendation](https://mlsanity.com/paper/2603.03761/) (2026)
- [A Survey on LLM-based News Recommender Systems](https://mlsanity.com/paper/2502.09797/) (2025)
- [Multi-modal Relational Item Representation Learning for Inferring Substitutable and Complementary Items](https://mlsanity.com/paper/2507.22268/) (2025)
- [Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Experts](https://mlsanity.com/paper/2508.05993/) (2025)
- [Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs](https://mlsanity.com/paper/2509.02017/) (2025)
- [Beyond Global Similarity: Towards Fine-Grained, Multi-Condition Multimodal Retrieval](https://mlsanity.com/paper/2603.01082/) (2026)
- [Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation](https://mlsanity.com/paper/2402.08921/) (2024)
- [Personalized Recommendation Models in Federated Settings: A Survey](https://mlsanity.com/paper/2504.07101/) (2025)
- [Multi-modal Adaptive Mixture of Experts for Cold-start Recommendation](https://mlsanity.com/paper/2508.08042/) (2025)
- [MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation](https://mlsanity.com/paper/2508.14515/) (2025)
- [SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation](https://mlsanity.com/paper/2605.18920/) (2026)
- [Bounded-Compute Multimodal Regression for Product-Rating Prediction](https://mlsanity.com/paper/2605.27737/) (2026)
- [UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style](https://mlsanity.com/paper/2402.10381/) (2024)
- [A Survey of Latent Factor Models in Recommender Systems](https://mlsanity.com/paper/2405.18068/) (2024)
- [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 Retrieval with Cauchy-Schwarz Divergence](https://mlsanity.com/paper/2509.21339/) (2025)
- [Approximate Diverse k-nearest Neighbor Search in Vector Database](https://mlsanity.com/paper/2510.27243/) (2025)

## Abstract (arXiv)

The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier and attribute information. With the emergence of multimedia services, such as short videos, news and etc., understanding these contents while recommending becomes critical. Besides, multimodal features are also helpful in alleviating the problem of data sparsity in RS. Thus, Multimodal Recommender System (MRS) has attracted much attention from both academia and industry recently. In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views. First, we conclude the general procedures and major challenges for MRS. Then, we introduce the existing MRS models according to four categories, i.e., Modality Encoder, Feature Interaction, Feature Enhancement and Model Optimization. Besides, to make it convenient for those who want to research this field, we also summarize the dataset and code resources. Finally, we discuss some promising future directions of MRS and conclude this paper. To access more details of the surveyed papers, such as implementation code, we open source a repository.