Collaborative Filtering Recommendation Based Trust Evaluation Method for Cloud Manufacturing Service

Jiang-Xiao Pei, Wang Mingxing, Liao Xiaobin, Yin Chao
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Abstract

In the cloud manufacturing (CMfg) model, users can get various high-quality, efficient manufacturing services (MSs) on-demand through the connection between the Internet of things and cloud platforms. While the problem of the reliable identification of MSs is one of the keys to the efficient operation of the cloud platform and the popularization and application of CMfg. To address this problem, a trust evaluation index system and a credible evaluation model considered the similarity and recommendation reliability between users’ behaviors are proposed in this paper. Based on the analysis of the factors that affect the credibility of MSs in the cloud environment, the analytic hierarchy process (AHP) is introduced to calculate the weight of each trusted evaluation index. In addition, a trusted estimation method based on collaborative filtering recommendation algorithm (CFRA) is proposed to solve the model and judge whether the MSs are trusted to the target user according to the obtained predictive valuation value. Finally, compared with PSO and GA, an example is employed to demonstrate the validity and effectiveness of the model and method, which can find a trusted MS for users and greatly save retrieval time.
基于协同过滤推荐的云制造服务信任评估方法
在云制造(CMfg)模式中,用户可以通过物联网与云平台的连接,按需获得各种优质、高效的制造服务。而MSs的可靠识别问题是云平台高效运行和CMfg推广应用的关键之一。针对这一问题,本文提出了考虑用户行为之间相似度和推荐可靠性的信任评价指标体系和可信度评价模型。在分析影响云环境下MSs可信度因素的基础上,引入层次分析法(AHP)计算各可信评价指标的权重。此外,提出了一种基于协同过滤推荐算法(CFRA)的可信估计方法,对模型进行求解,并根据得到的预测评价值判断是否信任目标用户。最后,通过与粒子群算法和遗传算法的比较,验证了该模型和方法的有效性,为用户找到了一个可信的MS,大大节省了检索时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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