The standout performance of disentangled graph convolutional networks, as evidenced in our study, offers a promising avenue for delivering transparent and actionable energy consumption forecasts, empowering WWTP operators to make decisions that enhance process sustainability .doi:10.1016/B978-0-443-28824-1.50454-3Louis AllenJoan C...
GCN等工作能很好的学节点emb,早起GCN在图的变换域进行卷积操作,如graph Fourier transformation(图傅里叶变换)。为了降低计算复杂度,有研究学者提出polynomial spectral filters(多项式变换域过滤器),之后进一步简化为线性过滤器。与此同时,有工作直接在graph 空间域上进行卷积。之后,在卷积操作时,引入注意力机制,自适应...
Code:https://github.com/xiangwang122 这篇文章主要借鉴了ICML 2019的一篇文章的工作:Disentangled Graph Convolutional Networks。 动机 这篇文章主要是将ICML 2019的工作应用在了推荐上,用户-物品交互关系的建模发展过程可以概括为① 单个ID(用户、物品)的embedding ② 融入个人历史信息的embedding(一阶连通性) ③ ...
Intent-aware Recommendation via Disentangled Graph Contrastive Learning论文阅读笔记 Abstract 存在的问题: 如何学习复杂和多样的意图,特别是当用户的行为在现实中通常不充分时 是不同的行为具有不同的意图分布,因此如何建立它们之间的关系,以建立一个更可解释的推荐系统。 本文方法: 在本文中,我们提出了通过解耦...
Zhang J, Shi X, Zhao S, King I (2019) Star-gcn: stacked and recon- structed graph convolutional networks for recommender systems. arXiv:1905.13129 45. Wang X, He X, Cao Y, Liu M, Chua T-S (2019) Kgat: knowledge graph attention network for recommendation. In: Proceedings of the ...
Current representation learning already benefits from the inductive biases of Convolutional Neural Networks (CNNs) (Lecun et al., 1998) and Recurrent Neural Networks (RNNs) (Graves et al., 2013). Outside of the visual domain, language has been modeled with recurrent neural networks that ...
Recommender systems aim to dig out the potential interests of users and find out items that might be connected with target users. Accuracy of the recommendation list is crucial for user-oriented applications. Many knowledge-based approaches combine graph
Disentangled Graph Convolutional NetworksJianxin MaPeng CuiKun KuangXin WangWenwu ZhuPMLRInternational Conference on Machine Learning
Abstract Many studies have shown that generative adversarial networks (GANs) can discover semantics at various levels of abstraction, yet GANs do not provide an intuitive way to show how they understand and control semantics. In order to identify interpretable directions in GAN’s latent space, both...
graph neural networksdisentanglementcollaborative filteringhigh-order relationshipsattention mechanismDisentangled Graph Convolutional Networks (disentangled GCNs) can explicitly learn embeddings according to users' intent, which helps increase both the accuracy and interpretability of recommendations. However, ...