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.

随着面向服务的制造模式的普及，在线制造服务平台的客户数量呈指数增长。为了提高在线平台的用户友好性和便利性，针对不同客户需求的个性化服务推荐是一种有效的手段。但是，由于制造服务通常以组合服务的形式出现，因此现有的基于Web服务的个性化推荐技术很难有效地应用。因此，本文提出了一种新颖的混合算法来解决制造服务组合（MSC）的个性化推荐。该算法综合考虑了QoS目标属性和客户偏好属性，解决了MSC优化个性化不足的问题。第一，提出了一种基于聚类的协同过滤（CCF）算法来量化客户偏好属性。其次，针对多属性MSC优化，提出了一种改进的面向个性化的第三代非支配排序遗传算法（PoNSGA-III）。最后，混合算法通过对客户偏好属性进行排名，为目标客户推荐最合适的解决方案。设计了一个详细的案例研究，以证明所提出的推荐算法的性能和实用性。混合算法通过对客户偏好属性进行排名，为目标客户推荐最合适的解决方案。设计了一个详细的案例研究，以证明所提出的推荐算法的性能和实用性。混合算法通过对客户偏好属性进行排名，为目标客户推荐最合适的解决方案。设计了一个详细的案例研究，以证明所提出的推荐算法的性能和实用性。