About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Collaborative Computing: Networking, Applications and Worksharing. 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part II

Research Article

Multi-dimensional Sequential Contrastive Learning for QoS Prediction

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-54528-3_28,
        author={Yuyu Yin and Qianhui Di and Yuanqing Zhang and Tingting Liang and Youhuizi Li and Yu Li},
        title={Multi-dimensional Sequential Contrastive Learning for QoS Prediction},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part II},
        proceedings_a={COLLABORATECOM PART 2},
        year={2024},
        month={2},
        keywords={QoS prediction Multi-dimensional contrastive learning Multi-task training},
        doi={10.1007/978-3-031-54528-3_28}
    }
    
  • Yuyu Yin
    Qianhui Di
    Yuanqing Zhang
    Tingting Liang
    Youhuizi Li
    Yu Li
    Year: 2024
    Multi-dimensional Sequential Contrastive Learning for QoS Prediction
    COLLABORATECOM PART 2
    Springer
    DOI: 10.1007/978-3-031-54528-3_28
Yuyu Yin1, Qianhui Di1, Yuanqing Zhang1, Tingting Liang1,*, Youhuizi Li1, Yu Li1
  • 1: School of Computer Science
*Contact email: liangtt@hdu.edu.cn

Abstract

Quality of service (QoS) is the main factor in service selection and recommendation, and it is influenced by dynamic factors, such as network condition and user location, and static factors represented by the invocation sequence at a fixed time slice. In order to jointly consider these two factors, this work proposes a multi-dimensional sequential contrastive learning framework named MDSCL, which applies contrastive learning method to learn the sequence representations of both user and time dimensionalities. An overlap crop augmentation strategy is proposed to obtain positive examples for user sequences and time sequences, respectively. Besides, MDSCL includes an integrated feature extractor that combines WaveNet and BiLSTM to facilitate the long short-term feature capturing. Extensive experiments on WSDREAM have been conducted to verify the effectiveness of our approach.

Keywords
QoS prediction Multi-dimensional contrastive learning Multi-task training
Published
2024-02-23
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-54528-3_28
Copyright © 2023–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL