[3] Wang, Xiang, et al. "Neural Graph Collaborative Filtering." SIGIR 2019. Knowledge Graph [1] Wang, Hongwei, et al. "RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems." CIKM 2018. (Wang, Hongwei, et al. "Exploring High-Order User Preference on the ...
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions 3. 快速浏览通道:思维导图 如果看不清楚的话可以去评论区下载源文件~ 1. Intro 1.1 推荐算法发展历史 1.1.1 Shallow Models 协同过滤CF,代表方法矩阵分解MF 《Matrix factorization techniques for recommender systems...
基本信息 论文题目:A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions 期刊信息:ACM, 2023 作者机构: Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, School of Information ...
Graph Convolutional Neural Networks for Web-Scale Recommender Systems LINK: https://arxiv.org/abs/1806.01973 CLASSIFICATION: RECOMMENDER-SYSTEM, GCN YEAR: Submitted on 6 Jun 2018 FROM: KDD 2018 WHAT P... [sampling] MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Sy...
Recommender systemRule learningGraph neural networkKnowledge graphTo alleviate the cold start problem caused by collaborative filtering in recommender systems, knowledge graphs (KGs) are increasingly employed by many methods as auxiliary resources. However, existing work incorporated with KGs cannot capture ...
Probabilistic Logic Neural Networks for Reasoning Meng Qu, Jian Tang arXiv 1906 3 图神经网络 Revisiting Semi-supervised Learning with Graph Embeddings Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov ICML 2016 Semi-Supervised Classification with Graph Convolutional Networks ...
He, C. et al. Fedgraphnn: a federated learning system and benchmark for graph neural networks.arXiv preprint arXiv:2104.07145(2021). Harper, F. M. & Konstan, J. A. The movielens datasets: History and context.ACM TIIS5, 1–19 (2015). ...
Graph Neural Networks for Social Recommendation Graph Neural Networks for Social Recommendation LINK: https://arxiv.org/abs/1902.07243 CLASSIFICATION: RECOMMENDER-SYSTEM, HETEROGENEOUS NETWORK, GCN YEAR: Submitted on 19 Feb 2019 (v1), last revised ... ...
Graph neural networks (GNNs) have shown great potential for personalized recommendation. At the core is to reorganize interaction data as a user-item bipartite graph and exploit high-order connectivity among user and item nodes to enrich their representations. While achieving great success, most exist...
推荐系统的发展可分为三个阶段:shallow models -> neural network-based models -> GNN models。其中: shallow models: 最早的推荐系统是利用协同过滤(Collaborative Filtering,CF)来计算user和item之间的相似度。后续在此基础上又提出了matrix factorization(MF)、factorization machine等方法。