A Two-Stream Light Graph Convolution Network-based Latent Factor Model for Accurate Cloud Service QoS Estimation-Reference-Cited by-同舟云学术

A Two-Stream Light Graph Convolution Network-based Latent Factor Model for Accurate Cloud Service QoS Estimation

Published:2022-11 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Data Mining (ICDM)
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Author:

Bi Fanghui¹,He Tiantian²,Luo Xin³

Affiliation:

1. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences Chongqing School, University of Chinese Academy of Sciences,Chongqing,China

2. Center for Frontier AI Research, Institute of High Performance Computing Agency for Science, Technology and Research (A*STAR),Singapore

3. Southwest University,College of Computer and Information Science,Chongqing,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10027565/10027596/10027700.pdf?arnumber=10027700

Reference19 articles.

1. LightGCN

2. Meta Matrix Factorization for Federated Rating Predictions

3. Deep graph convolutional networks with hybrid normalization for accurate and diverse recommendation”, in Proc. of 3rd Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD;guo,2021

4. Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

5. Variational Autoencoders for Collaborative Filtering

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