Abstract
Online job fairs and interviews have become commonplace due to the spread of the new coronavirus and the promotion of digital transformation (DX). Universities have also begun to take steps to improve the quality of job search activities for students. Among them, efforts to connect students and companies as 'matching' are increasing. Therefore, we decided to examine matching algorithms in job search and internship. As a typical example of a matching method, collaborative filtering is used, but it requires enough information to improve the accuracy of recommendation. However, since the data available in this research is limited and the number of data is not enough, we confirmed that the evaluation according to the hope is possible by using AHP. In addition, we verified the optimal allocation of limited resources, such as internship slots and job recommendation slots, by using maximum-weight matching. There were also confirmed that by reflecting the grades, students with good grades can be matched preferentially. This makes it possible to provide job search support and matching in line with the wishes of both students and companies.
https://ror.org/00vq1d511 
