Collaborative Filtering Recommendation Based Trust Evaluation Method for Cloud Manufacturing Service

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.