An End-to-End Deep Learning QoS Prediction Model Based on Temporal Context and Feature Fusion

被引:0
作者
Zhang, Peiyun [1 ]
Fan, Jiajun [1 ]
Chen, Yutong [1 ]
Huang, Wenjun [1 ]
Zhu, Haibin [2 ]
Zhao, Qinglin [3 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Engn Res Ctr Digital Forens, Sch Comp Sci, Minist Educ, Nanjing 210044, Peoples R China
[2] Nipissing Univ, Dept Comp Sci & Math, North Bay, ON P1B 8L7, Canada
[3] Macau Univ Sci & Technol, Sch Comp Sci & Engn, Taipa 999078, Peoples R China
基金
中国国家自然科学基金;
关键词
Quality of service; Feature extraction; Predictive models; Deep learning; Encoding; Accuracy; Time factors; Sparse matrices; Long short term memory; Context modeling; Service recommendation; deep neural network; QoS prediction; temporal context; FACTORIZATION; EFFICIENT;
D O I
10.1109/TSC.2025.3562324
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Existing end-to-end quality of service (QoS) prediction methods based on deep learning often use one-hot encodings as features, which are input into neural networks. It is difficult for the networks to learn the information that is conducive to prediction. Aiming at the above problem, an end-to-end deep learning QoS prediction model based on a temporal context and feature fusion is proposed. In the proposed model, three blocks are designed for QoS prediction. Firstly, a user-service encoding conversion block is designed to convert the one-hot encodings of users and services into the latent features of users and services, which can make full use of the data in sparse matrices. Then a time feature extraction block is designed to extract time features based on the time-varying characteristics of QoS values. Finally, the time features are fused with the latent features of users and services to predict QoS values. The experimental results show that on existing datasets, the proposed model has better prediction accuracy than other advanced methods in response time and throughput.
引用
收藏
页码:1232 / 1244
页数:13
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