A multi-objective service composition recommendation method for individualized customer: Hybrid MPA-GSO-DNN model

Abstract

With the development of manufacturing customization, unified manufacturing service recommendation is difficult to meet the customer’s individualized demand. To this end, the existing research hotspots focus on solving personalized service recommendation issues. However, the personalized recommendation for service composition is more complex compared with the existing single service recommendation. Especially in the case of less customer historical data, analyzing customer preference and recommending appropriate composite service is a difficult problem. Therefore, this paper proposes a hybrid MPA-GSO-DNN model based on manufacturing service to address the personalized recommendation problem for service composition. Firstly, a hybrid multi-objective preference analysis model and glowworm swarm optimization algorithm (MPA-GSO) is proposed to generate deep learning training set by analyzing customer preference and repetitively simulating the customer's selection process. The glowworm swarm optimization (GSO) algorithm is improved with dynamic step to solve the continuous multi-objective optimization in MPA-GSO. Secondly, a deep neural network (DNN) is structured to analyze candidate services and provide personalized recommendation. Finally, a case study is presented to demonstrate the performance and practicability of the proposed approach.