中国科学院重庆绿色智能技术研究院机构知识库

QQ客服 官方微博 反馈留言

	A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction
	Wu, Di1,2,3; Luo, Xin4,5; Shang, Mingsheng1,2; He, Yi6; Wang, Guoyin7; Wu, Xindong8,9
	2022-06-01
摘要	How to accurately predict unknown quality-of-service (QoS) data based on observed ones is a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) model has shown its efficiency in addressing this issue owing to its high accuracy and scalability. An LF model can be improved by identifying user and service neighborhoods based on user and service geographical information. However, such information can be difficult to acquire in most applications with the considerations of information security, identity privacy, and commercial interests in a real system. Besides, the existing LF model-based QoS predictors mostly ignore the reliability of given QoS data where noises commonly exist to cause accuracy loss. To address the above issues, this paper proposes a data-characteristic-aware latent factor (DCALF) model to implement highly accurate QoS predictions, where 'data-characteristic-aware' indicates that it can appropriately implement QoS prediction according to the characteristics of given QoS data. Its main idea is two-fold: a) it detects the neighborhoods and noises of users and services based on the dense LFs extracted from the original sparse QoS data, b) it incorporates a density peaks-based clustering method into its modeling process for achieving the simultaneous detections of both neighborhoods and noises of QoS data. With such designs, it precisely represents the given QoS data in spite of their sparsity, thereby achieving highly accurate predictions for unknown ones. Experimental results on two QoS datasets generated by real-world Web services demonstrate that the proposed DCALF model outperforms state-of-the-art QoS predictors, making it highly competitive in addressing the issue of Web service selection and recommendation.
关键词	Web Service quality-of-service QoS latent factor analysis density peak data-characteristic-aware missing data big data topological neighborhood noise data service selection data science
DOI	10.1109/TKDE.2020.3014302
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	34期号:6页码:2525-2538
通讯作者	Luo, Xin(luoxin21@gmail.com)
收录类别	SCI
WOS记录号	WOS:000789003800001
语种	英语