Design And Implementation Of A Disabled Employment Recommend System Based On Spring Boot

Under the background of the COVID-19 in the world,the situation of employment right now is not optimistic.As the foundation of our people’s livelihood,how to ensure employment has already become a great concern to the government.The disabled have a severe employment situation right now.Nowadays,the disabled people access to employment information is from the Federation websites.And most of these websites is not integrate the function of the recruitment website.There will be information overload for the disabled when they are trying to access the job information.With the rapid development of the Internet right now,those method is very inefficient and has been difficult to meet the needs of disabled and employers.In view of the above problems,this paper proposes and designs the employment recommendation system for the disabled.The system integrates the functions of registration,login,resume upload,resume analysis,job posting,job delivery and so on.And there are also the recommendation algorithm in the system,which can recommend jobs according to the situation of job seekers.It could reduce the occurrence of information overload problems.The system improving the job-seeking efficiency and meeting the needs of the disabled and the recruitment needs of enterprises.The system is designed according to the thought and process of software engineering,and necessarily design illustrations will be given in the paper.At last,the paper will complete the system function test and pressure test.This paper does not use the traditional single application architecture to build the system,but uses the popular microservice idea in the industry to split services by function,which reduces the coupling of system functions.Nacos framework will be used to discover and manage the service.And SpringBoot framework will be used to build individual services.The system recommendation algorithm uses the popular three-tier architecture of data processing layer,recall layer and ranking layer.Firstly,the training data is processed in the data processing layer to meet the needs of subsequent training.In the recall layer,we will use Item2 Vec Api in spark to quantify job information numerically,and job screening and recall are carried out according to the quantified job information.Finally,in the ranking layer,the ranking models based on gbdt and based on logistic regression are constructed respectively and the parameters are adjusted.The recommendation model is evaluated through the Auc and Logloss indicators,so as to estimate the click rate of the job sequence and achieve the final recommendation effect.