Research On C2B Personalized Customized Intelligent Recommendation For Manufacturing Enterprises

With the continuous improvement of production technology and the improvement of people's living standards,consumers' demand for product personalization has become increasingly strong.In this situation,the C2 B e-commerce model which concerning consumer's demands emerges as the times require.customization in C2 B is one of the important directions for the transformation and upgrading of manufacturing enterprises.However,the current development of personalized customization is week,and intelligent recommendation isn't introduced in the process to assist the user's customization.Besides,existing recommendation researchs mostly focuses on complete products and services and cannot be directly applied to personalized customization.This thesis tries to improve the classic intelligent recommendation system and algorithm,applying it to personalized customization in C2 B platform to guiding and assisting users to customize.Firstly,this thesis studies the development of C2 B customization in manufacturing companies,and analyzes the inapplicability of traditional intelligent recommendation : unable to meet the needs of consumers in the overall customization process,the requirements of manufacturing companies on the interactivity,the rationality and intensity of user's aggregation demands.Secondly,basing on the necessity of improvement,the improvement stategies of the intelligent recommendation system is proposed which include introducing step-by-step thinking,using history records,retaining two kinds of recommendation modules,introducing enterprise participation and using feedback mechanisms.Basing on these stategies,this thesis have improved the framework design and introduced new intelligent recommendation system.Thirdly,this thesis analyzes the limitations of the classic intelligent recommendation algorithm when applying in C2 B personalized customization and proposes an improved thinking of adopting implicit feedback data,independently running recommended steps,and considering the relevance of attributes.Based on this idea,it introduces the mechanism of the improved intelligent recommendation and the detailed deduction steps of the algorithm.Finally,by introducing a car customization example of the car manufacturing company,this thesis simulate the step-by-step intelligent recommendation algorithm,which verifies the practicability and effectiveness of the improved algorithm.