Knowledge Graph based Mass Customization Recommendation

Mass Customization has garnered widespread attention as a production strategy. This paper proposes a Knowledge Graph based Mass Customization Recommendation (KGMCR) method to meet this need. KGMCR captures user demands and product attributes and represents them in a graph structure. By leveraging Knowledge Graph (KG) and Neural Graph Collaborative Filtering (NGCF), it achieves accurate mass customization recommendations. To verify the efficacy of the proposed methodology, comprehensive experiments were conducted on the Amazon-Cell-Phone dataset. Experimental results show significant enhancement on large-scale personalized recommendations compared to baseline methods. The proposed KGMCR offers a novel avenue for enterprises to address mass customized demands. By integrating Knowledge Graph with mass customization recommendations, the potential to realize more intelligent and personalized product recommendations and customized experiences in the modern business environment is anticipated.