【论文笔记】Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions - 代码天地

【论文笔记】Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

编程语言 2023-07-29 17:22:03 阅读次数: 0

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转载自blog.csdn.net/weixin_45884316/article/details/124485736

【论文笔记】Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

Graph Neural Networks in Recommender Systems综述（文章搬运）

GNN 推荐系统综述 - Graph Neural Networks in Recommender Systems: A Survey

[PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems 论文详解KDD2018

论文笔记《STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems》

GCN:Graph Convolutional Neural Networks for Web-Scale Recommender Systems简介

[论文笔记]User-Centric Ultra-Dense Networks for 5G: Challenges , Methodologies, and Directions

Sequential Recommender Systems: Challenges, Progress and Prospects翻译和笔记

【论文笔记KDD2021】MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems

Federated Learning: Challenges, Methods, and Future Directions

推荐系统——Dual-regularized matrix factorization with deep neural networks for recommender systems

《Graph Neural Networks: A Review of Methods and Applications》阅读笔记

综述笔记：《Graph Neural Networks:A Review of Methods and Applications》-2019

论文笔记-Loop Closure Detection for Visual SLAM Systems Using Deep Neural Networks

推荐系统综述：A review on deep learning for recommender systems: challenges and remedies

ICDM 2020 Workshop on Neural Recommender Systems

FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS 论文笔记

「Deep Learning」Note on Graph Neural Networks: A Review of Methods and Applications

Paper：《Graph Neural Networks: A Review of Methods and Applications》翻译与解读

论文笔记 - Wide & Deep Learning for Recommender Systems

论文笔记之 Collaborative Deep Learning for Recommender Systems

论文笔记：Deep Matrix Factorization Models for Recommender Systems

综述：Federated Learning for 6G Communications:Challenges, Methods, and Future Directions

【论文导读】- Federated Graph Neural Networks: Overview, Techniques and Challenges（联邦图神经网络：概述、技术和挑战）

Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective

【文献阅读】 2019-IJCAI-Sequential Recommender Systems Challenges, Progress and Prospects

#Reading Paper# 【序列推荐综述】IJCAI‘19:Sequential Recommender Systems: Challenges, Progress and Prospects

论文阅读：《Wide & Deep Learning for Recommender Systems》

论文阅读: Wide & Deep Learning for Recommender Systems

《Wide & Deep Learning for Recommender Systems》论文总结

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