Research on manufacturing service recommendation method based on Product-based Neural Network
Authors
Lei Wang1, *, Lingrui Zhao1
1School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan, China
*Corresponding author.
Email: wanglei9455@whut.edu.cn
Corresponding Author
Lei Wang
Available Online 31 August 2024.
- DOI
- 10.2991/978-94-6463-490-7_13How to use a DOI?
- Keywords
- Product-based Neural Networks (PNN); Manufacturing Service Recommendation
- Abstract
Addressing the issue of overwhelming information on cloud-based manufacturing service platforms due to an excessive volume of service-related data, a manufacturing service recommendation method based on Product-based Neural Network (PNN) is proposed, which successfully solved the shortcomings of traditional recommendation technology in utilizing manufacturing service characteristics. By predicting the manufacturing service rating, the manufacturing service recommendation to the user is completed. Findings indicate that the PNN-driven rating forecast model markedly augments the accuracy of the recommendation system.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Lei Wang AU - Lingrui Zhao PY - 2024 DA - 2024/08/31 TI - Research on manufacturing service recommendation method based on Product-based Neural Network BT - Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024) PB - Atlantis Press SP - 109 EP - 114 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-490-7_13 DO - 10.2991/978-94-6463-490-7_13 ID - Wang2024 ER -