Personalized Manufacturing Service Recommendation Method Based on Federated Learning Framework

To solve the problem of the massive manufacturing services in the industrial cloud platform make it difficult for users to select services and the use of recommendation algorithm makes the platform have the risk of divulging user data, a personalized recommendation method of manufacturing services based on Federated Learning framework is proposed. The model-based Collaborative Filtering recommendation algorithm is adopted, and the scoring bias of users and services is considered in the matrix decomposition to improve the recommendation accuracy. Data used in the recommendation algorithm is classified and stored in the client and server, of which data containing users' privacy is retained locally. Local data is combined with data in the server to train the model after being desensitized by the encryption algorithm. Finally, users' scores on different attributes in manufacturing service are predicted. Users’ personalization tendency is further quantified by multi-attribute prediction scores according to the characteristics of manufacturing service quality. Pareto non-dominant solution set of the service is obtained through the intervention of the personalization tendency factor on the prediction score, and the recommended list of the manufacturing service is generated. The experiment shows that the recommendation method can obtain good prediction accuracy without compromising users’ privacy and the average prediction score of the recommendation service is high with users’ personalized preferences being considered, thus helping users better choose manufacturing services. © 2023 Editorial Office of Chinese Journal of Mechanical Engineering. All rights reserved.