A multi-attribute personalized recommendation method for manufacturing service composition with combining collaborative filtering and genetic algorithm. (January 2021)

• Record Type: Journal Article
• Authors: Liu, Zhengchao; Wang, Lei; Li, Xixing; Pang, Shibao
• Is Part Of: Journal of manufacturing systems. Volume 58(2021)Part A
• Page Start: 348
• Page End: 364
• Publication Date: 2021-01
• Subjects: Multi-attribute personalized recommendation -- Manufacturing service composition optimization -- Collaborative filtering -- Non-dominated sorting genetic algorithm; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2020.12.019
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Present a novel hybrid algorithm to address the personalized recommendation of manufacturing service composition. Establish a clustering-based collaborative filtering algorithm to quantify the customer preference attributes. Propose an improved non-dominated sorting genetic algorithm for the manufacturing service composition optimization. Abstract: With the popularity of service-oriented manufacturing mode, the customer quantities of the online manufacturing service platforms are growing exponentially. To improve the user-friendliness and convenience of online platforms, the personalized service recommendation for different customer requirement is an effective means. However, since manufacturing services usually appear in the form of composite services, existing Web service-based personalized recommendation technologies are difficult to be applied effectively. Therefore, this paper proposes a novel hybrid algorithm to address the personalized recommendation for manufacturing service composition (MSC). The algorithm solves the insufficient individualization defect of MSC optimization by comprehensively considering the QoS objective attributes and customer preference attributes. First, a Clustering-based Collaborative Filtering (CCF) algorithm is proposed to quantify the customer preference attributes. Second, an improved Personalization-oriented third generation Non-dominated Sorting Genetic Algorithm (PoNSGA-III) is presented for the multi-attribute MSC optimization. Finally, the hybrid algorithm recommends the most suitable solutions for the target customer through the ranking of customer preference attributes. A detailed case study is designed to demonstrate the performance and practicability of the proposed recommendation algorithm.