mlsanityData to 14 Oct 2026

Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey

arXiv 2404.00621 ↗First posted 31 Mar 2024cs.IR cs.MMcited by 25

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Abstract (arXiv)

Personalized recommendation serves as a ubiquitous channel for users to discover information tailored to their interests. However, traditional recommendation models primarily rely on unique IDs and categorical features for user-item matching, potentially overlooking the nuanced essence of raw item contents across multiple modalities such as text, image, audio, and video. This underutilization of multimodal data poses a limitation to recommender systems, especially in multimedia services like news, music, and short-video platforms. The recent advancements in large multimodal models offer new opportunities and challenges in developing content-aware recommender systems. This survey seeks to provide a comprehensive exploration of the latest advancements and future trajectories in multimodal pretraining, adaptation, and generation techniques, as well as their applications in enhancing recommender systems. Furthermore, we discuss current open challenges and opportunities for future research in this dynamic domain. We believe that this survey, alongside the curated resources, will provide valuable insights to inspire further advancements in this evolving landscape.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Mar 2024.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 10; 2026: 8.

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  1. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  2. 2025DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learningcited by 24
  3. 2024Understanding Layer Significance in LLM Alignmentcited by 19
  4. 2024The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspectivecited by 15
  5. 2025Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via Modality-decoupled Gradient Descentcited by 13
  6. 2024Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendationcited by 7
  7. 2025Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendationcited by 6
  8. 2026TSEmbed: Unlocking Task Scaling in Universal Multimodal Embeddingscited by 6
  9. 2025MOON2.0: Dynamic Modality-balanced Multimodal Representation Learning for E-commerce Product Understandingcited by 5
  10. 2025Diffusion Models in Recommendation Systems: A Surveycited by 3
  11. 2024COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendationscited by 2
  12. 2025Tree of Preferences for Diversified Recommendationcited by 2
13 more
  1. 2024Federated Vision-Language-Recommendation with Personalized Fusioncited by 1
  2. 2025MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendationcited by 1
  3. 2026Personalized Image Aesthetic Assessment via Preference-rich Sample Mining and Cohort Mergingcited by 1
  4. 2024ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generationcited by 0
  5. 2025MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimationcited by 0
  6. 2025MDE: Modality Discrimination Enhancement for Multi-modal Recommendationcited by 0
  7. 2025Intrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context Scenarioscited by 0
  8. 2026Cross-Modal Attention Network with Dual Graph Learning in Multimodal Recommendationcited by 0
  9. 2026VLM4Rec: Multimodal Semantic Representation for Recommendation with Large Vision-Language Modelscited by 0
  10. 2026MMRM: A Multiplex Multimodal Representation Model for Product Ranking in E-commerce Searchcited by 0
  11. 2026PaletteID: Prototype-Composed Semantic Identifiers for Multimodal CTR Predictioncited by 0
  12. 2026RecPFN: Prior-Fitted Networks for In-Context-Based Recommendationscited by 0
  13. 2026OT-FlowRec: Optimal Transport-Guided Multi-Bridge Flow for Multimodal Sequential Recommendationcited by 0

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  1. 2025Generate, Not Recommend: Personalized Multimodal Content Generation
  2. 2025Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation
  3. 2026RecoReward: Recommender-Guided Multimodal Description Generation for Recommendation
  4. 2024PMG : Personalized Multimodal Generation with Large Language Models
  5. 2025Bridging Domain Gaps between Pretrained Multimodal Models and Recommendations
  6. 2025Serendipitous Recommendation with Multimodal LLM
  7. 2026CAMMSR: Category-Guided Attentive Mixture of Experts for Multimodal Sequential Recommendation
  8. 2026A General Framework for Multimodal LLM-Based Multimedia Understanding in Large-Scale Recommendation Systems