基于协同过滤的web服务个性化QoS预测

Lingshuang Shao, Jing Zhang, Yong Wei, Junfeng Zhao, Bing Xie, Hong Mei
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引用次数: 463

摘要

许多研究者提出,在消费者选择服务时,不仅要考虑功能属性,也要考虑非功能属性,即服务质量(QoS)。消费者在选择之前需要对未使用的web服务的质量进行预测。通常,这种预测是基于其他消费者的经验。考虑到消费者不同的QoS体验,本文提出了一种基于协同过滤的方法,从消费者体验中进行相似性挖掘和预测。实验结果表明,该方法可以显著提高web服务QoS预测的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Personalized QoS Prediction forWeb Services via Collaborative Filtering
Many researchers propose that, not only functional but also non-functional properties, also known as quality of service (QoS), should be taken into consideration when consumers select services. Consumers need to make prediction on quality of unused web services before selecting. Usually, this prediction is based on other consumers' experiences. Being aware of different QoS experiences of consumers, this paper proposes a collaborative filtering based approach to making similarity mining and prediction from consumers' experiences. Experimental results demonstrate that this approach can make significant improvement on the effectiveness of QoS prediction for web services.
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