Document Type

Article

Publication Date

2025

DOI

10.1007/s42524-025-4038-x

Publication Title

Frontiers of Engineering Management

Volume

12

Issue

1

Pages

117-127

Abstract

Quality of Service (QoS) is a key factor for users when choosing cloud services. However, QoS values are often unavailable due to insufficient user evaluations or provider data. To address this, we propose a new QoS prediction method, Multi-source Feature Two-phase Learning (MFTL). MFTL incorporates multiple sources of features influencing QoS and uses a two-phase learning framework to make effective use of these features. In the first phase, coarse-grained learning is performed using a neighborhood-integrated matrix factorization model, along with a strategy for selecting high-quality neighbors for target users. In the second phase, reinforcement learning through a deep neural network is used to capture interactions between users and services. We conducted several experiments using the WS-Dream data set to assess MFTL's performance in predicting response time QoS. The results show that MFTL outperforms many leading QoS prediction methods.

Rights

© 2025 The Authors.

This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

ORCID

0000-0002-8686-8585 (Wu)

Original Publication Citation

Chen, F. Z., Yang, J., Feng, H. Y., Wu, H., & Li, M. Q. (2025). A two-phase learning approach integrated with multi-source features for cloud service QoS prediction. Frontiers of Engineering Management, 12(1), 117-127. https://doi.org/10.1007/s42524-025-4038-x

Download

Share

COinS