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

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arxiv 2302.04473 v1 pith:NCUT4Q7H submitted 2023-02-09 cs.IR cs.MM

Hongyu Zhou , Xin Zhou , Zhiwei Zeng , Lingzi Zhang , Zhiqi Shen This is my paper
classification cs.IRcs.MM
keywords modelscomprehensivedifferentframeworkimplicitinteractionsmodalitiesmultimodal
verification ladder T0 review T1 audit T2 compute T3 formal
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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

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. One Graph, Multiple Gains: Single High-Quality Item-Item Graph for Multimodal Recommendation

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    A single NCER-refined item-item graph, reused via adaptive gating, UI expansion, and discounted soft-positive BPR, improves multimodal recommendation accuracy and efficiency.

  2. FASH-iCNN: Making Editorial Fashion Identity Inspectable Through Multimodal CNN Probing

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    A multimodal CNN on 87,547 Vogue images classifies fashion houses at 78.2% top-1 accuracy, decades at 88.6%, and years at 58.3% with 2.2-year mean error, and shows texture and luminance carry most of the house-identit...

  3. Joint Behavior-guided and Modality-coherence Conditional Graph Diffusion Denoising for Multi Modal Recommendation

    cs.IR 2026-04 unverdicted novelty 6.0 of 10

    JBM-Diff applies conditional graph diffusion to remove preference-irrelevant multimodal noise and false-positive/negative behaviors, then augments training data via partial-order credibility scoring.

  4. TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning

    cs.AI 2026-04 unverdicted novelty 6.0 of 10

    TRU is a plug-and-play unlearning method for multimodal recommenders that applies ranking fusion, modality scaling, and layer isolation to achieve better retain-forget trade-offs than uniform baselines.

  5. Binge Watch: Reproducible Multimodal Benchmarks Datasets for Large-Scale Movie Recommendation on MovieLens-10M and 20M

    cs.IR 2026-02 conditional novelty 6.0 of 10

    M3L-10M and M3L-20M add plot, poster, audio, and video embeddings to MovieLens and release them publicly as reproducible multimodal benchmarks.

  6. Hi-SAM: A Hierarchical Structure-Aware Multi-modal Framework for Large-Scale Recommendation

    cs.AI 2026-02 conditional novelty 6.0 of 10

    Hi-SAM improves semantic-ID multimodal recommendation by disentangling shared versus modality-specific item codes and by letting transformers access history only through compressed anchor tokens.

  7. The Best is Yet to Come: Graph Convolution in the Testing Phase for Multimodal Recommendation

    cs.IR 2025-07 conditional novelty 6.0 of 10

    A multimodal recommender that trains without graph convolution and applies it only at test time outperforms graph-trained baselines while training much faster.

  8. URecJPQ: Memory-efficient Multimodal Recommendation Models through RecJPQ in Large-Scale Scenarios

    cs.IR 2026-06 unverdicted novelty 5.0 of 10

    URecJPQ compresses user and item embeddings via joint product quantization for multimodal top-k recommendation, cutting checkpoint size 86-98% and parameters 98-99% with average 8.5% recall drop across three datasets.

  9. Modality-Aware Identity Construction and Counterfactual Structure Learning for ID-Free Multimodal Recommendation

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    MAIL constructs modality-aware ID-free identities via dynamic positional encoding modulation and applies counterfactual structure learning with popularity penalization, yielding 7.81% Recall@10 and 12.81% NDCG@10 gain...

  10. Modality Alignment with Multi-scale Bilateral Attention for Multimodal Recommendation

    cs.IR 2025-09 conditional novelty 4.0 of 10

    MambaRec improves multimodal recommendation accuracy on Baby, Sports, and Clothing datasets through local dilated-attention alignment and global MMD/contrastive alignment.

  11. A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions

    cs.IR 2025-08 conditional novelty 4.0 of 10

    A scenario-oriented taxonomy of federated recommender systems that argues research should be organized around recommendation use cases rather than federated-learning abstractions.

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