Multimodal-Recommendation-Papers

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Multimodal-Recommendation-Papers

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This repository collects  several papers related to multimodal recommendation systems and annotated the key technologies used in some papers.

**KeyWords**: Multi-Modal Recommendation, Multimodal Recommendation, Micro-video Recommendation, Multimedia Recommendation

- [Category](#Category)
  - [Survey](#static-negative-sampling)
  - [Multi-Modal Recommendation System](#static-negative-sampling)
  - [Text-based Recommendation System](#static-negative-sampling)
  - [Image-based Recommendation System](#static-negative-sampling)

### Survey
- `arXiv(2025)`[Joint Modeling in Recommendations: A Survey](https://arxiv.org/pdf/2502.21195)**[PDF]**
- `Springer(2025)`[Multimodal Learning toward Recommendation](https://link.springer.com/book/10.1007/978-3-031-83188-1)**[Book]**
- `KDD(2024) `[Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey](https://dl.acm.org/doi/abs/10.1145/3637528.3671473). **[Survey]** **[[PDF](https://arxiv.org/pdf/2404.00621)]**
- `TORS(2024)`[Multimodal Pre-training for Sequential Recommendation via Contrastive Learning](https://dl.acm.org/doi/10.1145/3682075) **[Survey]** **[[PDF](https://dl.acm.org/doi/10.1145/3682075)]**
- `CSUR(2024)`[Multimodal Recommender Systems: A Survey](https://dl.acm.org/doi/abs/10.1145/3695461) **[Survey]** **[[PDF](https://arxiv.org/pdf/2302.03883)]**
- `arXiv (2025) `[A Survey on Multimodal Recommender Systems: Recent Advances and Future Directions](https://arxiv.org/pdf/2502.15711) **[Survey]** **[[PDF](https://arxiv.org/pdf/2502.15711)]**
- `TORS (2025) `[Formalizing Multimedia Recommendation through Multimodal Deep Learning](https://dl.acm.org/doi/pdf/10.1145/3662738) **[Survey]** **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3662738)]**

### Multi-Modal Recommendation System
- `SIGIR(2026)`Well Begun is Half Done: Training-Free and Model-Agnostic Semantically Guaranteed User Representation Initialization for Multimodal Recommendation **[[PDF](https://arxiv.org/abs/2604.14839)]**
- `TKDE(2026)`Training-free Graph-based Imputation of Missing Modalities in Multimodal Recommendation **[[PDF](https://arxiv.org/abs/2602.17354)]**
- `AAAI(2026)`MoToRec: Sparse-Regularized Multimodal Tokenization for Cold-Start Recommendation **[[PDF](https://arxiv.org/abs/2602.11062)]**
- `WWW(2026)`Personalized Parameter-Efficient Fine-Tuning of Foundation Models for Multimodal Recommendation **[[PDF](https://arxiv.org/abs/2602.09445)]**
- `TORS(2025)`Single-Branch Network Architectures to Close the Modality Gap in Multimodal Recommendation **[[PDF](https://arxiv.org/abs/2509.18807)]**
- `CIKM(2025)`Modality Alignment with Multi-scale Bilateral Attention for Multimodal Recommendation **[[PDF](https://arxiv.org/abs/2509.09114)]**
- `CIKM(2025)`Distribution-Guided Auto-Encoder for User Multimodal Interest Cross Fusion **[[PDF](https://arxiv.org/abs/2508.14485)]**
- `CIKM(2025)`Hypercomplex Prompt-aware Multimodal Recommendation **[[PDF](https://arxiv.org/abs/2508.10753)]**
- `CIKM(2025)`Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation **[[PDF](https://arxiv.org/abs/2508.04571)]**
- `RecSys(2025)`VL-CLIP: Enhancing Multimodal Recommendations via Visual Grounding and LLM-Augmented CLIP Embeddings **[[PDF](https://arxiv.org/abs/2507.17080)]**
- `RecSys(2025)`Improving Visual Recommendation on E-commerce Platforms Using Vision-Language Models **[[PDF](https://arxiv.org/abs/2510.13359)]**
- `arXiv (2025) `[Transferable Sequential Recommendation with Vanilla Cross-Entropy Loss](). **[Mamba]** **[[PDF](https://arxiv.org/pdf/2506.02916)]** 
- `TCSS (2025) `[DDRec: Dual Denoising Multimodal Graph Recommendation](). **[GNN+CL]** **[[PDF](https://ieeexplore.ieee.org/abstract/document/10768190)]** **[[CODE](https://github.com/Ricardo-Ping/DDRec)]**
- `Information Fusion (2025) `[PEARL: A dual-layer graph learning for multimodal recommendation](). **[GNN+CL]** **[[PDF](https://www.sciencedirect.com/science/article/pii/S1566253525002416)]** **[[CODE](https://github.com/Yuzhuo-Dang/PEARL)]**
- `arXiv (2025) `[Training-Free Graph Filtering via Multimodal Feature Refinement for Extremely Fast Multimodal Recommendation](). **[Graph filtering]** **[[PDF](https://arxiv.org/pdf/2503.04406)]**
- `arXiv (2025) `[Bridging Domain Gaps between Pretrained Multimodal Models and Recommendations](). **[Knowledge Transfer]** **[[PDF](https://arxiv.org/pdf/2502.15542)]** 
- `arXiv (2025) `[Explainable Multi-Modality Alignment for Transferable Recommendation](). **[Multi-modality alignment]** **[[PDF](https://openreview.net/pdf?id=5qxBSIA0l3)]** 
- `arXiv (2025) `[NoteLLM-2: Multimodal Large Representation Models for Recommendation](). **[Multimodal Large Language Model]** **[[PDF](https://arxiv.org/pdf/2405.16789)]** **[[CODE](https://github.com/Applied-Machine-Learning-Lab/NoteLLM)]**
- `MM (2024) `[Modality-Balanced Learning for Multimedia Recommendation](https://dl.acm.org/doi/10.1145/3664647.3680626). **[Knowledge Distillation]** **[[PDF](https://arxiv.org/pdf/2408.06360)]** **[[CODE](https://github.com/CRIPAC-DIG/Balanced-Multimodal-Rec)]**
- `MM (2024) `[DiffMM: Multi-Modal Diffusion Model for Recommendation](https://dl.acm.org/doi/10.1145/3664647.3681498). **[Multi-modal+Diffusion model]** **[[PDF](https://arxiv.org/pdf/2406.11781)]** **[[CODE](https://github.com/HKUDS/DiffMM)]**
- `CIKM (2024) `[AlignRec: Aligning and Training in Multimodal Recommendations](https://dl.acm.org/doi/abs/10.1145/3627673.3679626). **[Multi-modal Alignment]** **[[PDF](https://arxiv.org/pdf/2403.12384)]** **[[CODE](https://github.com/sjtulyf123/AlignRec_CIKM24)]**
- `CIKM (2024) `[Spectral and Geometric Spaces Representation Regularization for Multi-Modal Sequential Recommendation](https://dl.acm.org/doi/10.1145/3627673.3679647). **[Representation Optimization]** **[[CODE](https://github.com/WHUIR/E2ImgRec)]**
- `CIKM (2024) `[GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation](https://dl.acm.org/doi/10.1145/3627673.3679620). **[GNN+Multimodality Correlation]** **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3627673.3679620)]** **[[CODE](https://github.com/NanGongNingYi/GUME)]**
- `CIKM (2024) `[Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?](https://dl.acm.org/doi/abs/10.1145/3627673.3679898). **[GNN+Missing Modalities]** **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3627673.3679898)]** **[[CODE](https://github.com/sisinflab/Graph-Missing-Modalities)]**
- `TMM(2024) `[Disentangled Graph Variational Auto-Encoder for Multimodal Recommendation with Interpretability](https://dl.acm.org/doi/abs/10.1145/3695461). **[GNN+VAE]** **[[PDF](https://arxiv.org/pdf/2402.16110.pdf)]** **[[CODE](https://github.com/enoche/DGVAE)]**
- `TORS(2024) `[Formalizing Multimedia Recommendation through Multimodal Deep Learning](https://dl.acm.org/doi/abs/10.1145/3662738). **[Multimodal Deep Learning]** **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3662738)]** **[[CODE](https://github.com/sisinlab/Formal-MultiMod-Rec)]**
- `SIGIR(2024) `[Multimodality Invariant Learning for Multimedia-Based New Item Recommendation](https://dl.acm.org/doi/10.1145/3626772.3658596). **[Invariant Learning]** **[[CODE](https://github.com/HaoyueBai98/MILK)]**
- `SIGIR(2024) `[Disentangling ID and Modality Effects for Session-based Recommendation](https://dl.acm.org/doi/abs/10.1145/3626772.3657748). **[Disentangled Representation]** **[[PDF](https://arxiv.org/pdf/2404.12969)]** **[[CODE](https://github.com/Zhang-xiaokun/DIMO)]**
- `SIGIR(2024) `[Who To Align With: Feedback-Oriented Multi-Modal Alignment in Recommendation Systems](https://dl.acm.org/doi/abs/10.1145/3626772.3657701). **[Multi-modal Alignment]** **[[CODE](https://github.com/XMUDM/FETTLE)]**
- `SIGIR(2024) `[IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT](https://dl.acm.org/doi/pdf/10.1145/3626772.3657725). **[PEFT]** **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3626772.3657725)]** **[[CODE](https://github.com/GAIR-Lab/IISAN)]**
- `SIGIR(2024) `[EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation](https://dl.acm.org/doi/10.1145/3626772.3657890). **[Dataset+Multimodal]** **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3626772.3657890)]** **[[CODE](https://github.com/hezy18/EEG-SVRec)]**
- `SIGIR(2024) `[Dataset and Models for Item Recommendation Using Multi-Modal User Interactions](https://dl.acm.org/doi/10.1145/3626772.3657881). **[Multi-modal Interaction]** **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3626772.3657881)]** **[[CODE](https://github.com/simonebbruun/RS_multi_modal_user_interactions)]**
- `WWW(2024) `[PromptMM: Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning](https://dl.acm.org/doi/10.1145/3589334.3645359). **[Knowledge Distillation+Prompt-Tuning]** **[[PDF](https://arxiv.org/pdf/2402.17188)]** **[[CODE](https://github.com/HKUDS/PromptMM)]**
- `WWW(2024) `[Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima](https://dl.acm.org/doi/abs/10.1145/3589334.3645553). **[Robust Optimization]** **[[PDF](https://arxiv.org/pdf/2402.11262)]** **[[CODE](https://github.com/Qrange-group/Mirror-Gradient)]**
- `WWW(2024) `[MMPOI: A Multi-Modal Content-Aware Framework for POI Recommendations](https://dl.acm.org/doi/abs/10.1145/3589334.3645449). **[Content-Aware Framework]** **[[CODE](https://github.com/zzmylq/MMPOI)]**
- `KDD(2024) `[Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback](https://dl.acm.org/doi/abs/10.1145/3637528.3671703). **[Denoising+Alignment]** **[[PDF](https://arxiv.org/pdf/2406.12501)]** **[[CODE](https://github.com/XMUDM/DA-MRS)]**
- `KDD(2024) `[MMBee: Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour Expansion](https://dl.acm.org/doi/10.1145/3637528.3671511). **[Multi-modal Fusion+Behavior Expansion]** **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3637528.3671511)]**
- `RecSys(2024) `[A Multi-modal Modeling Framework for Cold-start Short-video Recommendation](https://dl.acm.org/doi/abs/10.1145/3640457.3688098). **[Cold-start+Multi-modal]**
- `AAAI(2024) `[LGMRec: Local and Global Graph Learning for Multimodal Recommendation]. **[GNN]** **[[PDF](https://arxiv.org/pdf/2312.16400.pdf)]** **[[CODE](https://github.com/georgeguo-cn/LGMRec)]**
- `KBS(2023) `[A holistic view on positive and negative implicit feedback for micro-video recommendation](https://www.sciencedirect.com/science/article/pii/S095070512301047X). **[RNN+GNN]** **[[CODE](https://github.com/gupanz/HMNet)]**
- `IJCNN(2023) `[A Multi-modal Multi-task based Approach for Movie Recommendation](http://ieeexplore.ieee.org/document/10191882). **[Multi-task Learning]** 
- `MM(2023) `[A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation](https://dl.acm.org/doi/10.1145/3581783.3611943). **[GNN]** **[[PDF](https://arxiv.org/pdf/2211.06924.pdf)]** **[[CODE](https://github.com/enoche/FREEDOM)]**
- `CIKM(2023) `[Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems](https://dl.acm.org/doi/10.1145/3583780.3614775). **[GNN]** **[[PDF](https://arxiv.org/pdf/2308.15980.pdf)]** **[[CODE](http://github.com/HoldenHu/MMSR)]**
- `SIGIR (2023) `[Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation](https://dl.acm.org/doi/10.1145/3539618.3591950). **[CNN+Transformer+GCN]** **[[PDF](https://arxiv.org/abs/2304.11979)]**
- `TKDE(2023) `[Beyond Co-occurrence: Multi-modal Session-based Recommendation](https://ieeexplore.ieee.org/document/10234637). **[CL+Transformer]** **[[PDF](https://arxiv.org/abs/2309.17037)]** **[[CODE](https://github.com/Zhang-xiaokun/MMSBR)]**
- `WWW(2023) `[Bootstrap Latent Representations for Multi-modal Recommendation](http://dl.acm.org/doi/10.1145/3543507.3583251). **[CL+GNN]** **[[PDF](https://arxiv.org/pdf/2207.05969.pdf)]** **[[CODE](https://github.com/enoche/BM3)]**
- `Applied Soft Computing(2023) `[Collaborative Recommendation Model Based on Multi-modal Multi-view Attention Network: Movie and literature cases ](https://www.sciencedirect.com/science/article/pii/S1568494623005367). **[KG+BERT+GAT]** **[[PDF](https://arxiv.org/pdf/2305.15159.pdf)]**
- `MM(2023) `[Contrastive Intra- and Inter-Modality Generation for Enhancing Incomplete Multimedia Recommendation](https://dl.acm.org/doi/10.1145/3581783.3612362). **[CL]** 
- `ICMR(2023) `[Cross-View Sample-Enriched Graph Contrastive Learning Network for Personalized Micro-video Recommendation](https://dl.acm.org/doi/abs/10.1145/3591106.3592220). **[GCL]** 
- `TMM(2023) `[Disentangled Multimodal Representation Learning for Recommendation](https://ieeexplore.ieee.org/document/9930669). **[Disentangled Representation Learning]** **[[PDF](https://arxiv.org/pdf/2203.05406.pdf)]**
- `TOIS(2023) `[Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation](https://dl.acm.org/doi/abs/10.1145/3617827). **[ Meta-Learning]** **[[CODE](https://github.com/hanliu95/MetaMMF)]**
- `MULTIMEDIA SYSTEMS(2023) `[Empowering neural collaborative filtering with contextual features for multimedia recommendation](https://link.springer.com/article/10.1007/s00530-023-01107-9).
- `MM(2023) `[Enhancing Adversarial Robustness of Multi-modal Recommendation via Modality Balancing](https://dl.acm.org/doi/10.1145/3581783.3612337).
- `ECAI(2023) `[Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal Recommendation](https://ebooks.iospress.nl/doi/10.3233/FAIA230631). **[GNN]** **[[PDF](https://arxiv.org/pdf/2301.12097.pdf)]** **[[CODE](https://github.com/hongyurain/DRAGON)]**
- `SAC(2023) `[Graph Convolutional Neural Network for Multimodal Movie Recommendation](http://dl.acm.org/doi/10.1145/3555776.3577853). **[GCN]** 
- `SIGIR(2023) `[Learning fine-grained user interests for micro-video recommendation](https://dl.acm.org/doi/abs/10.1145/3539618.3591713). **[[CODE](https://github.com/tsinghua-fib-lab/FRAME)]**
- `TOMM(2023) `[Learning the User's Deeper Preferences for Multi-modal Recommendation Systems](http://dl.acm.org/doi/10.1145/3573010). **[GCN]** 
- `SIGIR(2023) `[LightGT: A Light Graph Transformer for Multimedia Recommendation](http://dl.acm.org/doi/10.1145/3539618.3591716). **[GCN+Transformer ]** **[[CODE](https://github.com/Liuwq-bit/LightGT)]**
- `TOIS(2023) `[MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation](http://dl.acm.org/doi/10.1145/3544106). **[GCN]** **[[CODE](https://github.com/kangliu1225/MEGCF)]**
- `WWW (2023) `[MEMER - Multimodal Encoder for Multi-signal Early-stage Recommendations](http://dl.acm.org/doi/10.1145/3543873.3587679).
- `ESWA(2023) `[Meta-path based graph contrastive learning for micro-video recommendation](https://www.sciencedirect.com/science/article/pii/S0957417423002142). **[GNN+CL]** 
- `Multimedia Tools and Applications(2023) `[Micro video recommendation in multimodality using dual-perception and gated recurrent graph neural network](https://link.springer.com/article/10.1007/s11042-023-17093-z). **[GRU+GNN]** 
- `SIGIR(2023) `[Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation](http://dl.acm.org/doi/10.1145/3539618.3591729). **[Stable Learning]** 
- `MM(2023) `[MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation](https://dl.acm.org/doi/10.1145/3581783.3611967). **[Transformer]** **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3581783.3611967)]**
- `Electronics(2023) `[MKGCN: Multi-Modal Knowledge Graph Convolutional Network for Music Recommender Systems](https://www.mdpi.com/2079-9292/12/12/2688). **[KG+GCN]** **[[CODE](https://github.com/QuXiaolong0812/mkgcn)]**
- `TKDE(2023) `[MM-FRec: Multi-Modal Enhanced Fashion Item Recommendation](https://ieeexplore.ieee.org/abstract/document/10100905). **[GNN]** 
- `WWW (2023) `[MMMLP: Multi-modal Multilayer Perceptron for Sequential Recommendations](http://dl.acm.org/doi/10.1145/3543507.3583378). **[MLP-Mixer]** **[[CODE](https://github.com/Applied-Machine-Learning-Lab/MMMLP)]**
- `MM(2023) `[Modal-aware Bias Constrained Contrastive Learning for Multimodal Recommendation](https://dl.acm.org/doi/10.1145/3581783.3612568). **[CL]** 
- `KBS(2023) `[Multi-Head multimodal deep interest recommendation network](http://linkinghub.elsevier.com/retrieve/pii/S0950705123004392). **[DIN]** **[[PDF](https://arxiv.org/pdf/2110.10205.pdf)]**
- `Applied Intelligence(2023) `[Multimodal collaborative graph for image recommendation](https://link.springer.com/article/10.1007/s10489-022-03304-x). **[GNN]** 
- `CSS(2023) `[Multimodal Contrastive Transformer for Explainable Recommendation](http://ieeexplore.ieee.org/document/10132382). **[Transformer]** 
- `TMM(2023) `[Multimodal Graph Contrastive Learning for Multimedia-Based Recommendation](https://ieeexplore.ieee.org/abstract/document/10075502). **[GCL]** **[[CODE](https://github.com/hfutmars/MGCL)]**
- `ISDA(2023) `[Multi-modal Knowledge Graph Convolutional Network for Recommendation](http://link.springer.com/chapter/10.1007/978-3-031-35507-3_43). **[KG+GNN]** 
- `CIKM(2023) `[Multi-modal Mixture of Experts Represetation Learning for Sequential Recommendation](https://dl.acm.org/doi/10.1145/3583780.3614978). **[MoE+SA]** **[[CODE](https://github.com/RUCAIBox/M3SRec)]**
- `Mathematics(2023) `[Multimodal movie recommendation system using deep learning](https://www.mdpi.com/2227-7390/11/4/895). **[DNN]** 
- `arXiv(2023) `[Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning ](https://arxiv.org/abs/2303.11879). **[CNN]** 
- `PLOS ONE(2023) `[Multi-modal recommendation algorithm fusing visual and textual features](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0287927). **[Attention Mechanism]** 
- `WWW (2023) `[Multi-Modal Self-Supervised Learning for Recommendation](http://dl.acm.org/doi/10.1145/3543507.3583206). **[SSL]** **[[PDF](https://arxiv.org/pdf/2302.10632.pdf)]** **[[CODE](https://github.com/HKUDS/MMSSL)]**
- `MM(2023) `[Multi-View Graph Convolutional Network for Multimedia Recommendation](https://dl.acm.org/doi/10.1145/3581783.3613915). **[GCN]** **[[PDF](https://arxiv.org/ftp/arxiv/papers/2308/2308.03588.pdf)]** **[[CODE](https://github.com/demonph10/MGCN)]**
- `MM(2023) `[Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation](https://dl.acm.org/doi/10.1145/3581783.3612091). **[Transformer+Distillation]** **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3581783.3612091)]** **[[CODE](https://github.com/xyliugo/ODMT)]**
- `MM(2023) `[Pareto Invariant Representation Learning for Multimedia Recommendation](https://dl.acm.org/doi/10.1145/3581783.3612591). **[Invariant Learning]** **[[PDF](https://arxiv.org/pdf/2308.04706.pdf)]**
- `MM(2023) `[Prior-Guided Accuracy-Bias Tradeoff Learning for CTR Prediction in Multimedia Recommendation ](https://dl.acm.org/doi/10.1145/3581783.3613801).
- `Information Fusion(2023) `[Prompt-based and weak-modality enhanced multimodal recommendation](https://www.sciencedirect.com/science/article/pii/S1566253523003056). **[[CODE](https://github.com/hello-dx/POWERec)]**
- `MM(2023) `[Semantic-Guided Feature Distillation for Multimodal Recommendation.](https://dl.acm.org/doi/10.1145/3581783.3611886). **[Distillation Learning]** **[[PDF](https://arxiv.org/pdf/2308.03113.pdf)]** **[[CODE](https://github.com/HuilinChenJN/SGFD)]**
- `MM(2023) `[Task-Adversarial Adaptation for Multi-modal Recommendation](https://dl.acm.org/doi/10.1145/3581783.3612391). **[Multi-task learning]** **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3581783.3612391)]** **[[CODE](https://github.com/TL-UESTC/TAA.)]**
- `EMNLP(2023) `[VIP5: Towards Multimodal Foundation Models for Recommendation](https://arxiv.org/abs/2305.14302). **[P5 recommendation paradigm]** **[[PDF](https://arxiv.org/pdf/2305.14302.pdf)]** **[[CODE](https://github.com/jeykigung/VIP5)]**
- `arXiv(2023) `[A Content-Driven Micro-Video Recommendation Dataset at Scale](https://arxiv.org/abs/2309.15379). **[[PDF](https://arxiv.org/pdf/2309.15379.pdf)]** **[[CODE](https://github.com/westlake-repl/MicroLens)]**
- `MM (2022) `[Adaptive Anti-Bottleneck Multi-Modal Graph Learning Network for Personalized Micro-video Recommendation](https://dl.acm.org/doi/abs/10.1145/3503161.3548420). **[GNN]** 
- `NEUROCOMPUTING(2022) `[Binary multi-modal matrix factorization for fast item cold-start recommendation](https://linkinghub.elsevier.com/retrieve/pii/S0925231222009870). **[[CODE](https://github.com/pcm1217/BMMF)]**
- `MM(2022) `[Breaking Isolation: Multimodal Graph Fusion for Multimedia Recommendation by Edge-wise Modulation](https://dl.acm.org/doi/abs/10.1145/3503161.3548399). **[GNN]** **[[CODE](https://github.com/feiyuchen7/EgoGCN)]**
- `MM (2022) `[EliMRec: Eliminating Single-modal Bias in Multimedia Recommendation](https://dl.acm.org/doi/abs/10.1145/3503161.3548404). **[GNN]** **[[CODE](https://github.com/Xiaohao-Liu/EliMRec)]**
- `MM (2022) `[From Abstract to Details: A Generative Multimodal Fusion Framework for Recommendation](https://dl.acm.org/doi/abs/10.1145/3503161.3548366). **[Generative Learning]** 
- `SMC(2022) `[Graph Network based Approaches for Multi-modal Movie Recommendation System](http://ieeexplore.ieee.org/document/9945488). **[GNN]** 
- `TMM(2022) `[Heterogeneous Graph Contrastive Learning Network for Personalized Micro-Video Recommendation](http://ieeexplore.ieee.org/document/9712249/). **[GCL]** 
- `arXiv(2022) `[Implicit semantic-based personalized micro-videos recommendation](https://arxiv.org/abs/2205.03297).
- `MM(2022) `[Invariant Representation Learning for Multimedia Recommendation](https://dl.acm.org/doi/10.1145/3503161.3548405). **[Invariant Learning]** **[[CODE](https://github.com/nickwzk/InvRL)]**
- `TKDE(2022) `[Latent Structure Mining With Contrastive Modality Fusion for Multimedia Recommendation](http://ieeexplore.ieee.org/document/9950351). **[GNN]** **[[PDF](https://arxiv.org/pdf/2111.00678.pdf)]** **[[CODE](https://github.com/CRIPAC-DIG/MICRO)]**
- `MM(2022) `[Learning Hybrid Behavior Patterns for Multimedia Recommendation](https://dl.acm.org/doi/abs/10.1145/3503161.3548119). **[GNN]** 
- `DASFAA(2022) `[M-3-IB: A Memory-Augment Multi-modal Information Bottleneck Model for Next-Item Recommendation](https://link.springer.com/chapter/10.1007/978-3-031-00126-0_2). **[[CODE](https://github.com/kk97111/M3IB)]**
- `ICME(2022) `[M3Rec: Cross-Modal Context Enhanced Micro-Video Recommendation with Mutual Information Maximization](http://ieeexplore.ieee.org/document/9859663/). **[GNN]** 
- `CIKM(2022) `[MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation](https://dl.acm.org/doi/abs/10.1145/3511808.3557387).
- `SIGIR(2022) `[MM-rec: Visiolinguistic model empowered multimodal news recommendation](https://dl.acm.org/doi/abs/10.1145/3477495.3531896). **[Transformer]** 
- `ICMR(2022) `[Multi-modal contrastive pre-training for recommendation](https://dl.acm.org/doi/abs/10.1145/3512527.3531378). **[GNN+CL]** 
- `ISMIS(2022) `[Multimodal Deep Learning and Fast Retrieval for Recommendation](https://link.springer.com/chapter/10.1007/978-3-031-16564-1_6).
- `CCET(2022) `[Multi-modal graph attention network for video recommendation](http://ieeexplore.ieee.org/document/9906399). **[GNN]** 
- `SIGIR(2022) `[Multi-modal Graph Contrastive Learning for Micro-video Recommendation](https://dl.acm.org/doi/abs/10.1145/3477495.3532027). **[GCL]** **[[PDF](https://eprints.gla.ac.uk/269019/1/269019.pdf)]**
- `TCSS(2022) `[Multimodal Hierarchical Graph Collaborative Filtering for Multimedia-Based Recommendation](http://ieeexplore.ieee.org/document/9989417). **[GNN]** **[[CODE](https://github.com/hfutmars/MHGCF)]**
- `CIKM(2022) `[Multimodal meta-learning for cold-start sequential recommendation](https://dl.acm.org/doi/abs/10.1145/3511808.3557101). **[ Meta Learning]** **[[CODE](https://github.com/RUCAIBox/MML.)]**
- `SACAIR(2022) `[Multi-modal Recommendation System with Auxiliary Information](http://link.springer.com/chapter/10.1007/978-3-031-22321-1_8). **[Transformer]** 
- `SIGIR(2022) `[Next point-of-interest recommendation with auto-correlation enhanced multi-modal transformer network](https://dl.acm.org/doi/abs/10.1145/3477495.3531905). **[Transformer]** 
- `APPLIED INTELLIGENCE(2022) `[Preference-corrected multimodal graph convolutional recommendation network](https://link.springer.com/article/10.1007/s10489-022-03681-3). **[GCN]** 
- `APPLIED INTELLIGENCE(2022) `[Robust multi-objective visual bayesian personalized ranking for multimedia recommendation](https://link.springer.com/article/10.1007/s10489-021-02355-w).
- `TMM(2022) `[Self-supervised Learning for Multimedia Recommendation](https://ieeexplore.ieee.org/document/9811387). **[SSL]** **[[CODE](https://github.com/zltao/SLMRec)]**
- `IJCNN(2022) `[Towards Developing a Multi-Modal Video Recommendation System](http://ieeexplore.ieee.org/document/9892382). **[KG]** **[[CODE](https://github.com/SriramPingali/Multi-Modal-Recommendation-System)]**
- `AI-HCI(2021) `[A deep learning based multi-modal approach for images and texts recommendation](https://link.springer.com/chapter/10.1007/978-3-030-77772-2_23). **[DBN]** 
- `Information Sciences(2021) `[A two-stage embedding model for recommendation with multimodal auxiliary information](http://www.sciencedirect.com/science/article/pii/S0020025521009270?via%3Dihub#s0015). **[GCN]** 
- `TMM(2021) `[DualGNN: Dual Graph Neural Network for Multimedia Recommendation](https://ieeexplore.ieee.org/abstract/document/9662655). **[GNN]** **[[CODE](https://github.com/wqf321/dualgnn)]**
- `ISM(2021) `[Enhancing Personalised Recommendations with the Use of Multimodal Information](http://ieeexplore.ieee.org/document/9666124).
- `TMM(2021) `[Heterogeneous Hierarchical Feature Aggregation Network for Personalized Micro-Video Recommendation](http://ieeexplore.ieee.org/document/9360479/). **[GNN]** 
- `TMM(2021) `[Hierarchical User Intent Graph Network for Multimedia Recommendation](http://ieeexplore.ieee.org/document/9453189). **[GCN]** **[[CODE](https://github.com/weiyinwei/HUIGN)]**
- `INTERNET RESEARCH(2021) `[Interpretable video tag recommendation with multimedia deep learning framework](https://www.emerald.com/insight/content/doi/10.1108/INTR-08-2020-0471/full/html). **[CNN]** 
- `MM(2021) `[Mining Latent Structures for Multimedia Recommendation](https://dl.acm.org/doi/10.1145/3474085.3475259). **[GCN]** **[[PDF](https://arxiv.org/pdf/2104.09036.pdf)]** **[[CODE](https://github.com/CRIPAC-DIG/LATTICE)]**
- `arXiv(2021) `[MM-Rec: Multimodal News Recommendation](https://arxiv.org/abs/2104.07407). **[Transformer]** 
- `MTA(2021) `[Multimedia recommendation using Word2Vec-based social relationship mining](https://link.springer.com/article/10.1007/s11042-019-08607-9). **[Word2Vec]** 
- `TKDE(2021) `[Multi-Modal Discrete Collaborative Filtering for Efficient Cold-Start Recommendation](https://ieeexplore.ieee.org/abstract/document/9429954).
- `ICME(2021) `[Multimodal Disentangled Representation for Recommendation](https://ieeexplore.ieee.org/document/9428193).
- `BigMM(2021) `[Multimodal Fusion Based Attentive Networks for Sequential Music Recommendation](https://ieeexplore.ieee.org/abstract/document/9643207). **[Disentangled Representation Learning]** 
- `KBS(2021) `[Multi-modal knowledge-aware reinforcement learning network for explainable recommendation](https://www.sciencedirect.com/science/article/pii/S0950705121004792). **[KG]** 
- `International Journal of Information Technology(2021) `[Multimodal trust based recommender system with machine learning approaches for movie recommendation](https://link.springer.com/article/10.1007/s41870-020-00553-2). **[BPNN+SVM+DNN]** 
- `TMM(2021) `[Multi-Modal Variational Graph Auto-Encoder for Recommendation Systems](https://ieeexplore.ieee.org/document/9535249). **[GCN+VAE]** 
- `Information Sciences(2021) `[Multi-modal visual adversarial Bayesian personalized ranking model for recommendation](http://www.sciencedirect.com/science/article/pii/S0020025521004801?via%3Dihub#s0035). **[Adversarial Learning]** 
- `PRL(2021) `[Multimodal-adaptive hierarchical network for multimedia sequential recommendation](http://www.sciencedirect.com/science/article/pii/S0167865521003202?via%3Dihub). **[LSTM]** 
- `IEEE BIG DATA(2021) `[Object Interaction Recommendation with Multi-Modal Attention-based Hierarchical Graph Neural Network](https://ieeexplore.ieee.org/document/9671426/citations#citations). **[Attention Mechanism+Transformer+GNN]** **[[CODE](https://github.com/gaosaroma/MM-AHGNN)]**
- `APPLIED SCIENCES(2021) `[Predicting Implicit User Preferences with Multimodal Feature Fusion for Similar User Recommendation in Social Media](https://www.mdpi.com/2076-3417/11/3/1064). **[Autoencoder+CNN]** 
- `MM (2021) `[Pre-training Graph Transformer with Multimodal Side Information for Recommendation](http://dl.acm.org/doi/10.1145/3474085.3475709?__cf_chl_rt_tk=.GZxo1HtH3q_FDbUg0kpJUUGiVUT._ih_rfqk.CQvFY-1693913332-0-gaNycGzNEzs). **[ Graph Transformer]** **[[PDF](https://arxiv.org/pdf/2010.12284.pdf)]**
- `KDD(2021) `[SEMI: A Sequential Multi-Modal Information Transfer Network for E-Commerce Micro-Video Recommendations](https://dl.acm.org/doi/abs/10.1145/3447548.3467189). **[CL]** 
- `ESWA(2021) `[Session-based news recommendations using SimRank on multi-modal graphs](https://www.sciencedirect.com/science/article/pii/S0957417421004693). **[GNN]** 
- `GLOBECOM(2021) `[Social Recommendation System with Multimodal Collaborative Filtering](https://ieeexplore.ieee.org/abstract/document/9685648). **[GNN+LSTM]** 
- `arXiv(2021) `[Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation](https://arxiv.org/abs/2107.05124). **[Transformer]** 
- `Chaos, Solitons & Fractals(2021) `[BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformer](https://www.sciencedirect.com/science/article/pii/S0960077921006147). **[Transformer]** **[[PDF](https://arxiv.org/abs/2007.07229)]**
- `TCSS(2020) `[AMNN: Attention-Based Multimodal Neural Network Model for Hashtag Recommendation](http://ieeexplore.ieee.org/document/9082813). **[Attention mechanism]** **[[CODE](http://github.com/w5688414/AMNN)]**
- `IEEE ACCESS(2020) `[An Enhanced Multi-Modal Recommendation Based on Alternate Training With Knowledge Graph Representation](https://ieeexplore.ieee.org/abstract/document/9264240). **[KG]** 
- `IEEE BIG DATA(2020) `[Complementary Recommendations Using Deep Multi-modal Embeddings For Online Retail](https://ieeexplore.ieee.org/document/9378363). **[LSTM]** 
- `TMM(2020) `[Context-Dependent Propagating-Based Video Recommendation in Multimodal Heterogeneous Information Networks](https://ieeexplore.ieee.org/abstract/document/9136871). **[GNN]** 
- `IJCNN(2020) `[Enhancing Music Recommendation with Social Media Content: an Attentive Multimodal Autoencoder Approach](https://ieeexplore.ieee.org/document/9206894). **[Attention mechanism+Autoencoder]** 
- `MM(2020) `[Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback](https://dl.acm.org/doi/10.1145/3394171.3413556). **[GNN]** **[[PDF](https://arxiv.org/pdf/2111.02036.pdf)]** **[[CODE](https://github.com/weiyinwei/GRCN)]**
- `KBS(2020) `[Hashtag our stories: Hashtag recommendation for micro-videos via harnessing multiple modalities](https://www.sciencedirect.com/science/article/pii/S0950705120303798). **[DNN]** 
- `TMM(2020) `[Learning and fusing multiple user interest representations for micro-video and movie recommendations](https://ieeexplore.ieee.org/abstract/document/9025233). **[Attention mechanism]** **[[CODE](https://github.com/Ocxs/MUIR)]**
- `IPM(2020) `[MGAT: Multimodal Graph Attention Network for Recommendation](https://www.sciencedirect.com/science/article/abs/pii/S0306457320300182). **[GAT]** **[[CODE](https://github.com/zltao/MGAT)]**
- `FGCS(2020) `[Multi-modal Bayesian embedding for point-of-interest recommendation on location-based cyber-physical–social networks](https://www.sciencedirect.com/science/article/pii/S0167739X17310191).
- `CIKM(2020) `[Multi-modal Knowledge Graphs for Recommender Systems](https://dl.acm.org/doi/10.1145/3340531.3411947). **[KG]** **[[PDF](https://zheng-kai.com/paper/cikm_2020_sun.pdf)]**
- `arXiv (2020) `[Multimodal Topic Learning for Video Recommendation](https://arxiv.org/abs/2010.13373). **[CNN+Transformer+GNN]** 
- `KDD(2020) `[PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest](https://dl.acm.org/doi/10.1145/3394486.3403280). **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3394486.3403280)]**
- `TII(2020) `[Recommendation by Users’ Multimodal Preferences for Smart City Applications](https://ieeexplore.ieee.org/abstract/document/9152003).
- `APPLIED SCIENCES-BASEL(2020) `[Recommendations for Different Tasks Based on the Uniform Multimodal Joint Representation](https://www.mdpi.com/2076-3417/10/18/6170).
- `IEEE ACCESS(2020) `[Sentiment Enhanced Multi-Modal Hashtag Recommendation for Micro-Videos](http://ieeexplore.ieee.org/document/9076071). **[SA]** 
- `IEEE(2020) `[Sequential Recommendation with a Pre-trained Module Learning Multi-modal Information](https://ieeexplore.ieee.org/document/9291549). **[SA]** 
- `DSN-W(2020) `[TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems](http://ieeexplore.ieee.org/document/9151572). **[Adversarial Learning]** **[[CODE](https://github.com/sisinflab/TAaMR)]**
- `ICME(2020) `[User Conditional Hashtag Recommendation for Micro-Videos](https://ieeexplore.ieee.org/abstract/document/9102824). **[Attention network]** 
- `MM(2020) `[What Aspect Do You Like: Multi-scale Time-aware User Interest Modeling for Micro-video Recommendation](https://dl.acm.org/doi/abs/10.1145/3394171.3413653). **[Attention Network]** 
- `arXiv(2019) `[Interest-Related Item Similarity Model Based on Multimodel Data for Top-N Recommendation](https://arxiv.org/abs/1902.05566). **[[PDF](https://arxiv.org/ftp/arxiv/papers/1902/1902.05566.pdf)]**
- `CIKM(2019) `[Long-tail hashtag recommendation for micro-videos with graph convolutional network](https://dl.acm.org/doi/abs/10.1145/3357384.3357912). **[GCN]** 
- `MM(2019) `[MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video](https://dl.acm.org/doi/10.1145/3343031.3351034). **[GCN]** **[[CODE](https://github.com/weiyinwei/MMGCN)]**
- `DSE(2019) `[MMM: Multi-source Multi-net Micro-video Recommendation with Clustered Hidden Item Representation Learning](https://link.springer.com/article/10.1007/s41019-019-00101-4). **[CNN+PMF]** 
- `IOTJ(2019) `[Multimodal Representation Learning for Recommendation in Internet of Things](https://ieeexplore.ieee.org/document/8832204). **[CNN]** 
- `ICMEW(2019) `[Multi-modal Representation Learning for Short Video Understanding and Recommendation](http://ieeexplore.ieee.org/document/8795067). **[MLP]** 
- `DASFAA(2019) `[Multi-source Multi-net Micro-video Recommendation with Hidden Item Category Discovery](https://link.springer.com/chapter/10.1007/978-3-030-18579-4_23). **[CNN+PMF]** 
- `SIGIR(2019) `[Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network](https://dl.acm.org/doi/10.1145/3331184.3331254). **[Attention Network]** 
- `MM(2019) `[Personalized Hashtag Recommendation for Micro-videos](https://dl.acm.org/doi/abs/10.1145/3343031.3350858). **[GNN]** **[[PDF](https://arxiv.org/pdf/1908.09987.pdf)]** **[[CODE](https://github.com/weiyinwei/PHR_GCN)]**
- `ICIG(2019) `[Personalized Micro-video Recommendation Based on Multi-modal Features and User Interest Evolution](http://link.springer.com/chapter/10.1007/978-3-030-34110-7_51). **[GRU+Attention Mechanism+MLP]** 
- `ICME(2019) `[Sequential Behavior Modeling for Next Micro-Video Recommendation with Collaborative Transformer](https://ieeexplore.ieee.org/abstract/document/8784862). **[Transformer]** 
- `MM(2019) `[User Diverse Preference Modeling by Multimodal Attentive Metric Learning](https://dl.acm.org/doi/10.1145/3343031.3350953). **[Attention Mechanism+Metric Learning]** **[[PDF](https://arxiv.org/pdf/1908.07738.pdf)]** **[[CODE](https://github.com/liufancs/MAML)]**
- `WWW(2019) `[User-Video Co-Attention Network for Personalized Micro-video Recommendation](https://dl.acm.org/doi/10.1145/3308558.3313513). **[Attention Mechanism]** 
- `MM(2019) `[Vision-Language Recommendation via Attribute Augmented Multimodal Reinforcement Learning](https://dl.acm.org/doi/10.1145/3343031.3350935). **[Reinforcement Learning]** 
- `MTA(2018) `[LGA: latent genre aware micro-video recommendation on social media](https://link.springer.com/article/10.1007/s11042-017-4827-2). **[CNN+BRNN]** 
- `UMAP(2018) `[Multi-modal adversarial autoencoders for recommendations of citations and subject labels](https://dl.acm.org/doi/abs/10.1145/3209219.3209236). **[Adversarial Autoencoder]** **[[PDF](https://arxiv.org/pdf/1907.12366)]** **[[CODE](https://github.com/lgalke/aae-recommender)]**
- `IJCAI(2017) `[Hashtag Recommendation for Multimodal Microblog Using Co-Attention Network.](http://www.qizhang.info/paper/ijcai2017hashtag.pdf). **[Attention Mechanism+Metric Learning]** 
- `DLRS(2017) `[A deep multimodal approach for cold-start music recommendation](https://dl.acm.org/doi/abs/10.1145/3125486.3125492). **[CNN]** **[[PDF](https://arxiv.org/pdf/1706.09739.pdf%7D)]** **[[CODE](https://github.com/sergiooramas/tartarus)]**
- `SIGIR(2017) `[Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention](https://dl.acm.org/doi/abs/10.1145/3077136.3080797). **[Attention Mechanism+Metric Learning]** **[[PDF](https://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/Attentive%20Collaborative%20Filtering%20Multimedia%20Recommendation%20with%20Item-%20and%20Component-Level%20Attention.pdf)]**
- `ESWA(2017) `[Preference dynamics with multimodal user-item interactions in social media recommendation](https://www.sciencedirect.com/science/article/pii/S0957417417300052). **[MF]** 
- `TMM(2017) `[Social-aware movie recommendation via multimodal network learning](https://ieeexplore.ieee.org/abstract/document/8010448). **[Metric Learning]**
- `NIPS(2013) `[Deep content-based music recommendation](https://papers.nips.cc/paper_files/paper/2013/hash/b3ba8f1bee1238a2f37603d90b58898d-Abstract.html). **[CNN]** **[[PDF](https://papers.nips.cc/paper_files/paper/2013/file/b3ba8f1bee1238a2f37603d90b58898d-Paper.pdf)]**
### Text-based Recommendation System
- `KDD(2019) `[DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation](https://dl.acm.org/doi/10.1145/3292500.3330906). **[Neural Network + Attention Mechanism]** 
- `WSDM(2019) `[Gated Attentive-Autoencoder for Content-Aware Recommendation](https://dl.acm.org/doi/10.1145/3289600.3290977). **[Gated Attentive-Autoencoder]** **[[PDF](https://arxiv.org/pdf/1812.02869.pdf)]** **[[CODE](https://github.com/allenjack/GATE)]**
- `WWW(2018) `[Neural Attentional Rating Regression with Review-level Explanations](https://dl.acm.org/doi/10.1145/3178876.3186070). **[Attention Mechanism]** **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3178876.3186070)]**
- `KDD(2018) `[Multi-Pointer Co-Attention Networks for Recommendation](https://dl.acm.org/doi/10.1145/3219819.3220086). **[Co-Attention Networks ]** **[[PDF](https://arxiv.org/pdf/1801.09251.pdf)]**
- `WSDM(2017) `[Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://dl.acm.org/doi/10.1145/3018661.3018665). **[Neural Network]** **[[PDF](https://arxiv.org/pdf/1701.04783.pdf)]**
- `RecSys(2016) `[Convolutional Matrix Factorization for Document Context-Aware Recommendation](https://dl.acm.org/doi/10.1145/2959100.2959165). **[CNN]** **[[PDF](https://dsail.kaist.ac.kr/files/RecSys16.pdf)]** **[[CODE](https://github.com/cartopy/ConvMF)]**
- `IJCAI(2016) `[Collaborative Multi-Level Embedding Learning from Reviews for Rating Prediction](https://www.ijcai.org/Abstract/16/424). **[Topic Model]** **[[PDF](https://www.ijcai.org/Proceedings/16/Papers/424.pdf)]**
- `RecSys(2013) `[Hidden factors and hidden topics: understanding rating dimensions with review text](https://dl.acm.org/doi/10.1145/2507157.2507163). **[Topic Model]** **[[PDF](https://cs.stanford.edu/people/jure/pubs/reviews-recsys13.pdf)]**
- `KDD(2011) `[Collaborative topic modeling for recommending scientific articles](https://dl.acm.org/doi/10.1145/2020408.2020480). **[Topic Model]** **[[PDF](https://www.cs.columbia.edu/~blei/papers/WangBlei2011.pdf)]**
### Image-based Recommendation System
- `TKDE(2022) `[Modeling Product’s Visual and Functional Characteristics for Recommender Systems](https://ieeexplore.ieee.org/document/9084265). **[MF]** **[[PDF](https://data-science.ustc.edu.cn/_upload/article/files/c4/4f/10f4da284171a6275429698edccf/e932db6e-a7f3-47e9-af55-58be7a665e11.pdf)]** **[[CODE](https://github.com/wubinzzu/VFPMF)]**
- `TKDE(2020) `[Adversarial Training Towards Robust Multimedia Recommender System](http://ieeexplore.ieee.org/document/8618394). **[Adversarial Learning]** **[[CODE](https://github.com/duxy-me/AMR.)]**
- `Information Sciences(2019) `[Visual appearance or functional complementarity: Which aspect affects your decision making?](https://www.sciencedirect.com/science/article/pii/S0020025518308077) **[MF]**  **[[CODE](https://github.com/wubin7019088/VRPMF)]**
- `AAAI(2016) `[VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback](https://ojs.aaai.org/index.php/AAAI/article/view/9973). **[BPR]** **[[PDF](https://ojs.aaai.org/index.php/AAAI/article/download/9973/9832)]**

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