这种注意力机制能够自适应地调整邻居节点的重要性,使得GAT在处理复杂的图结构时更加灵活。 GGNN(Gated Graph Neural Network):在更新步骤中采用门控循环单元,使得节点特征能够有效地捕捉序列信息。GGNN 在所有节点上多次执行递归函数,当应用于大型图时可能会面临可扩展性问题。 、deep factorization machine(DeepFM),实际上就是将神经网络和前面提到的CF、FM结合了起来。 缺点:仍然没有考虑到数据的高阶结构信息。(要注意到用户的偏好...
Recommender system, one of the most successful commercial applications of the artificial intelligence, whose user-item interactions can naturally fit into graph structure data, also receives much attention in applying graph neural networks (GNNs). We first summarize the most recent advancements of GNNs...
Since most of the information essentially has graph structure and GNNs have superiority in representation learning, the field of utilizing graph neural network in recommender systems is flourishing. This article aims to provide a comprehensive review of recent research efforts on graph neural network ...
Graph neural network-based collaborative filtering In this section, we first present the general GCF framework. We then show that SVD and SVD++ can be expressed under GCF with node embedding via graph neural network. To address the problem of dealing with variable size inputs in the information...
论文题目:MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems 论文链接:http://keg.cs.tsinghua.edu.cn/jietang/publications/KDD21-Huang-et-al-MixGCF.pdf 论文代码:https://github.com/huangtinglin/MixGCF
MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems 来源:KDD 2021 摘要:协同过滤的一个基本挑战是从隐式反馈中提取负信号,但基于GNN的协同过滤的负采样在很大程度上尚未被探索。在本文中,作者通过同时利用用户-项目图结构与GNN的聚合过程来研究负采样。作者提出了一种具有泛用性...
而就 2020的情况来看,这个趋势还在不断扩大。总之,Graph Neural Network (简称“GNN”)在2019- 2020年之间,力压 Deep Learning、GAN等,成为各大顶会的增长热词,且GNN在各个领域越来越受到欢迎,包括社交网络、知识图谱、推荐系统,甚...