Title: A context-aware factorisation machine approach for accurate QoS prediction

Authors: Wenyu Tang; Mingdong Tang; Jianguo Xie

Addresses: School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, 510006, China ' School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, 510006, China ' School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, 510006, China

Abstract: Web services are very popular in constructing software systems on the internet. With the increasing number of web services with similar functionalities, quality of service (QoS) becomes a crucial concern in web service selection. However, QoS values of web services may be unknown to users for service providers used not to publish them. Moreover, QoS values usually depend on the contexts of services and their users, such as locations and network conditions. Therefore, to accurately acquire QoS values of web services is a challenge. By collecting and exploring web services' historical QoS records, this paper proposes an accurate QoS prediction approach based on context-aware factorisation machines (CAFM). The approach adapts the classic factorisation machine model by leveraging the context information of services and users. Experimental results based on a real-world QoS dataset validate the performance of the proposed approach.

Keywords: QoS prediction; context-aware; factorisation machines; service selection.

DOI: 10.1504/IJCSE.2024.138455

International Journal of Computational Science and Engineering, 2024 Vol.27 No.3, pp.314 - 325

Received: 19 Oct 2022
Accepted: 06 Dec 2022
Published online: 03 May 2024 *

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