Research on Distributed Intelligent Recommendation Algorithm for Personalized Customization from Consumer to Enterprise
AUTHORS
Manolova Andon,American University Bulgaria Amer Univ Bulgaria, Bulgaria
Mutafchiev Svetlozar,American University Bulgaria Amer Univ Bulgaria, Bulgaria
ABSTRACT
With the continuous improvement of production technology and the improvement of people's living standards, mass-produced products can hardly meet people's growing material and cultural needs, and users' individual needs are becoming stronger. The development of personalized customization from Consumer to Business (C2B) is one of the important ways for manufacturing enterprises to transform and upgrade. However, at this stage, companies cannot personalize customization, and users have not introduced smart recommendations to assist in customization when they participate in the customization process. The existing research on a personalized intelligent recommendation for manufacturing enterprises is only for the optimization and adjustment of the algorithm itself, and does not effectively combine the characteristics of personalized customization step by step, and attributes and attribute customization content are independent and interrelated. To better guide users' product customization and decision-making, users can accurately describe their needs and improve customization efficiency. In the personalized customization of products, the idea of intelligent recommendation is introduced, and improvements are made based on the original item-based collaborative filtering recommendation algorithm. A step-by-step intelligent recommendation algorithm suitable for C2B personalized customization is proposed, and a car customization case is introduced. This paper introduces the internal mechanism and recommendation steps of the recommendation algorithm in detail and introduces an example to simulate the recommendation process of the algorithm. Experimental results and recommendation results show that the recommendation algorithm for C2B personalized customization in this paper is feasible and effective.
KEYWORDS
C2B personalized customization, Intelligent recommendation, Collaborative filtering, Decision support
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