Collaborative QoS prediction with context-sensitive matrix factorization
Collaborative QoS prediction with context-sensitive matrix factorization
复制标题
通过上下文相关矩阵分解进行协作 QoS 预测
DOI：
10.1016/j.future.2017.06.020
复制
发表时间：
2018-05-01
影响因子：
7.5
通讯作者：
Hsu, Ching-Hsien
中科院分区：
文献类型：
--
作者：
Wu, Hao;Yue, Kun;Hsu, Ching-Hsien
随着互联网上服务的爆炸式增长，如何获得个性化的云/物联网服务质量，帮助用户选择合适的服务成为一个热点问题。为了解决这一问题，借鉴推荐系统的思想，提出了协同服务质量预测。然而，由于上下文因素在服务质量评估中起着至关重要的作用，如何将上下文因素融入到现有的算法中来实现上下文感知的服务质量预测仍然是一个具有挑战性的问题。本文提出了一种通用的上下文敏感矩阵分解方法(CSMF)来进行协同服务质量预测。通过考虑服务调用的复杂性，CSMF同时对用户与服务和环境与环境的交互进行建模，并充分利用了服务质量数据中的隐式和显式上下文因素。实验结果表明，CSMF在预测精度方面明显优于目前最先进的方法。特别是，当服务质量数据非常稀疏时，CSMF更有效和更健壮。(C)2017爱思唯尔B.V.保留所有权利。
How to obtain personalized quality of cloud/IoT services and assist users selecting the appropriate service has become a hot issue with the explosion of services on the Internet. Collaborative QoS prediction is proposed to address this issue by borrowing ideas from recommender systems. However, there is still a challenging problem as how to incorporate contextual factors into existing algorithms to realize context aware QoS prediction as contextual factors play a crucial role in QoS assessment. In this paper, we propose a general context-sensitive matrix-factorization approach (CSMF) to make collaborative QoS prediction. By considering the complexity of service invocations, CSMF models the interactions of users-to-services and environment-to-environment simultaneously, and make full use of implicit and explicit contextual factors in the QoS data. Experimental results show that CSMF significantly outperforms the-state-of-art methods in metric of prediction accuracy. Particularly, when the QoS data is very sparse, CSMF is more effective and robust. (C) 2017 Elsevier B.V. All rights reserved.