Research Article
Multi-dimensional Sequential Contrastive Learning for QoS Prediction
@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
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.
