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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 10 Pith papers

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

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

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

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    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

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

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