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 ...
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 Informati...
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...
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 ...
Convolutional neural networks on graphs with fast localized spectral filteringView more references Cited by (103) A deep reinforcement learning based long-term recommender system 2021, Knowledge-Based Systems Show abstract Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotat...
使用图神经网络进行知识图谱的深度学习 Deep learning with knowledge graphs using graph neural networks 热度: KGAT:KnowledgeGraphAtenionNeworkfor Recommendaion XiangWang NationalUniversityo Singapore xiangwang@u.nus.edu XiangnanHe ∗ Universityo ScienceandTechnology ...
Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains a ...
system works. As most of the information is in graph structure and graph neural networks (GNNs) have a specialty in representation learning, the field of utilizing GNN in recommender systems is expanding. This chapter provides knowledge of GNN-based recommender systems and state-of-the-art models...
For graph neural networks, the existing methods consist of two categories, spectral models and spatial ones. We then discuss the motivation of applying graph neural networks into recommender systems, mainly consisting of the high-order connectivity, the structural property of data, and the enhanced ...
"Graph Neural Networks for Social Recommendation." WWW 2019. [4] Wu, Qitian, et al. "Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender System." WWW 2019. [5] Wu, Le, et al. "A Neural Influence Diffusion Model for Social ...