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Journal : Proceedings of International Conference on Multidiciplinary Research

Job Recommendation System Using the Content-Based Filtering Method Ade Rifqi Hawari; Munawir Munawir; Zulfan Zulfan; Dedi Satria Dedi Satria; Susmanto Susmanto
International Conference on Multidisciplinary Research Vol 5, No 2 (2022): ICMR
Publisher : Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/pic-mr.v5i2.5419

Abstract

On the xyz application that is already running, it is a job vacancy portal. In this system there are has two users, as business accounts and job seekers. Where business accounts can post vacancies and job seekers can apply for vacancies. In this running system, when job seekers display the vacancies list, there is a list of the latest vacancies that have been posted by business people. The problem with the list of vacancies is that the list of vacancies that appear is still not in accordance with the wishes of the user as the job seeker account, thus reducing the interest in application users. There-fore, a job vacancy recommendation system is needed that can correct deficiencies in the list of vacancies that are displayed to users as job seekers. The purpose of this research to be able to pro-duce recommendations for suitable job vacancies. In this research, the authors use the content-based filtering method.   Keywords: Recommendation System, Machine Learning, Content-based Filtering