mlsanityData to 7 Oct 2026

Multimodal Recommender Systems: A Survey

arXiv 2302.03883 ↗First posted 8 Feb 2023cs.IR cs.AIcited by 36

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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.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). Builds on 37 of its 95 references that are in the map. First posted on arXiv 8 Feb 2023.

Builds on

37 of 95 references.

References · 37

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  4. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  5. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  6. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  7. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  8. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,004
  9. 2021Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendationcited by 173
  10. 2019Learning Disentangled Representations for Recommendationcited by 110
  11. 2021Mining Latent Structures for Multimedia Recommendationcited by 79
  12. 2019Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendationcited by 70
25 more
  1. 2019POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashioncited by 63
  2. 2021GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedbackcited by 58
  3. 2022Bootstrap Latent Representations for Multi-modal Recommendationcited by 55
  4. 2022A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendationcited by 53
  5. 2022Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completioncited by 44
  6. 2021Non-invasive Self-attention for Side Information Fusion in Sequential Recommendationcited by 39
  7. 2022TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedbackcited by 27
  8. 2022CrossCBR: Cross-view Contrastive Learning for Bundle Recommendationcited by 26
  9. 2022A Review of Modern Fashion Recommender Systemscited by 25
  10. 2020Bundle Recommendation with Graph Convolutional Networkscited by 24
  11. 2021Understanding Chinese Video and Language via Contrastive Multimodal Pre-Trainingcited by 23
  12. 2021Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendationcited by 18
  13. 2019Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approachcited by 17
  14. 2020Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendationcited by 17
  15. 2023MMRec: Simplifying Multimodal Recommendationcited by 17
  16. 2018Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generationcited by 16
  17. 2022Disentangled Multimodal Representation Learning for Recommendationcited by 15
  18. 2021Content-driven Music Recommendation: Evolution, State of the Art, and Challengescited by 12
  19. 2019NRPA: Neural Recommendation with Personalized Attentioncited by 7
  20. 2022MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendationcited by 7
  21. 2021MM-Rec: Multimodal News Recommendationcited by 6
  22. 2021Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendationscited by 3
  23. 2022VLSNR:Vision-Linguistics Coordination Time Sequence-aware News Recommendationcited by 3
  24. 2019Interest-Related Item Similarity Model Based on Multimodal Data for Top-N Recommendationcited by 2
  25. 2022Implicit semantic-based personalized micro-videos recommendationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 17; 2026: 10.

36 papers.

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  1. 2023A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challengecited by 55
  2. 2024LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendationcited by 47
  3. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 31
  4. 2024Language Representations Can be What Recommenders Need: Findings and Potentialscited by 18
  5. 2025Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendationcited by 15
  6. 2023Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case Studycited by 12
  7. 2025Hyperbolic Diffusion Recommender Modelcited by 9
  8. 2025Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendationcited by 6
  9. 2023Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudgingcited by 4
  10. 2025Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learningcited by 4
  11. 2024GPRec: Bi-level User Modeling for Deep Recommenderscited by 3
  12. 2026Inference-Time Dynamic Modality Selection for Incomplete Multimodal Classificationcited by 3
24 more
  1. 2026AgentSelect: Benchmark for Narrative Query-to-Agent Recommendationcited by 3
  2. 2025Multi-modal Relational Item Representation Learning for Inferring Substitutable and Complementary Itemscited by 2
  3. 2025Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Expertscited by 2
  4. 2025Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDscited by 2
  5. 2026Beyond Global Similarity: Towards Fine-Grained, Multi-Condition Multimodal Retrievalcited by 2
  6. 2024Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendationcited by 1
  7. 2025A Survey on LLM-based News Recommender Systemscited by 1
  8. 2025Personalized Recommendation Models in Federated Settings: A Surveycited by 1
  9. 2025Multi-modal Adaptive Mixture of Experts for Cold-start Recommendationcited by 1
  10. 2025MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendationcited by 1
  11. 2026SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendationcited by 1
  12. 2026Bounded-Compute Multimodal Regression for Product-Rating Predictioncited by 1
  13. 2024UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Stylecited by 0
  14. 2024A Survey of Latent Factor Models in Recommender Systemscited by 0
  15. 2025Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR Challengecited by 0
  16. 2025A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directionscited by 0
  17. 2025Cross-Modal Retrieval with Cauchy-Schwarz Divergencecited by 0
  18. 2025Approximate Diverse k-nearest Neighbor Search in Vector Databasecited by 0
  19. 2025Structural and Disentangled Adaptation of Large Vision Language Models for Multimodal Recommendationcited by 0
  20. 2026DReX: An Explainable Deep Learning-based Multimodal Recommendation Frameworkcited by 0
  21. 2026TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearningcited by 0
  22. 2026Learning Robust Recommenders from Noisy Implicit Feedback via GMM-Weighted Bayesian Transition Matrixcited by 0
  23. 2026UniNote: A Unified Embedding Model for Multimodal Representation and Rankingcited by 0
  24. 2026Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decodingcited by 0