Two-Stream Graph Convolutional Network-Incorporated Latent Feature Analysis-Reference-Cited by-同舟云学术

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Two-Stream Graph Convolutional Network-Incorporated Latent Feature Analysis

Published:2023-07-01 Issue:4 Volume:16 Page:3027-3042
ISSN:1939-1374
Container-title:IEEE Transactions on Services Computing
language:
Short-container-title:IEEE Trans. Serv. Comput.

Author:

Bi Fanghui¹,He Tiantian²,Xie Yuetong³,Luo Xin¹^ORCID

Affiliation:

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

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

3. Faculty of Data Science, City University of Macau, Macau, China

Funder

National Natural Science Foundation of China

CAAI Huawei MindSpore Open Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/4629386/10213383/10035508.pdf?arnumber=10035508

Reference83 articles.

1. Instance-Frequency-Weighted Regularized, Nonnegative and Adaptive Latent Factorization of Tensors for Dynamic QoS Analysis

2. Neural Graph Collaborative Filtering

3. HAP: A Hybrid QoS Prediction Approach in Cloud Manufacturing Combining Local Collaborative Filtering and Global Case-Based Reasoning

4. Graph convolutional matrix completion;berg;Proc ACM SIGKDD Int Conf Knowledge Discovery and Data Mining,2018

5. Investigating QoS of Real-World Web Services

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