Papers with Code 아카이브

Multi-modal Recommendation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 3
보존된 평가 기록: 30

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/CRIPAC-DIG/LATTICE

공식 코드

Mining Latent Structures for Multimedia Recommendation

https://github.com/CrawlScript/Torch-MGDCF

공식 코드

MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering

https://github.com/WuXinglong-HIT/CLIPER

공식 코드

Towards Bridging the Cross-modal Semantic Gap for Multi-modal Recommendation

https://github.com/crawlscript/mig-gt

공식 코드

Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation

https://github.com/enoche/bm3

공식 코드

Bootstrap Latent Representations for Multi-modal Recommendation

https://github.com/enoche/freedom

공식 코드

A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation

https://github.com/gusye1234/pytorch-light-gcn

공식 코드

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/hkuds/mmssl

공식 코드

Multi-Modal Self-Supervised Learning for Recommendation

https://github.com/hujunxianligong/MGDCF

공식 코드

MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering

https://github.com/kuandeng/LightGCN

공식 코드

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/neon-jing/guider

공식 코드

Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration

https://github.com/oysterqaq/umair-fps

공식 코드

UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style

https://github.com/recsys-lab/rag-visualrec

공식 코드

RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation

https://github.com/weiyinwei/grcn

공식 코드

GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

https://github.com/xyliugo/odmt

공식 코드

Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation

https://github.com/yuyangye/mllm-msr

공식 코드

Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation

https://github.com/HKUDS/RCL

Multi-Modal Self-Supervised Learning for Recommendation

https://github.com/JiahaoWuGit/DcRec

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/LehengTHU/Agent4Rec

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/PreferredAI/cornac

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/Trantin84/NGCF-Tin

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/Wuyxin/LightGCN-parallelized-version

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/abkds/r-ecommender

VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

https://github.com/apat1n/LightGCN-Pytorch

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/arogers1/VBPR

VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

https://github.com/domainxz/top-k-rec

VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

https://github.com/enoche/mmrec

Bootstrap Latent Representations for Multi-modal Recommendation

https://github.com/gusye1234/LightGCN-PyTorch

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/ialab-puc/CuratorNet

VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

https://github.com/jchanxtarov/vbpr

VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

https://github.com/jinfeng-xu/fkan-gcf

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/lucapantea/LightGCN

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/massquantity/LibRecommender

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/microsoft/recommenders

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/nathann3/better_than_netflix_movie_recommender

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/sayamsingla2000/LightGCN_MovieLens

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.