Implementasi Metode CTR Estimate Dan GLMIX Rangking Model Untuk Rekomendasi Lowongan Pekerjaan - Mawaddah Harahap (Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP, 2022)
The conventional job search and recruitment processes are considered less effective and efficient in terms of cost and time. To assist job seekers in getting the desired job and assist job providers in obtaining potential candidates, a recommendation system is needed. An ideal job recommendation system should be able to fulfill several objectives such as recommending the most relevant jobs to users. To solve these problems, a recommendation system method can be applied. One method that can be applied is the Click Through Rate (CTR) Estimate and Generalized Linear Mixed (GLMix) ranking model method. In this method, the concept of job boosting and penalization will be used to set the scoring value of each job. If a job is predicted to have a value smaller than the minimum target value, then the scoring value of the job will be carried out by a boosting process. The result of this research is a job recommendation website that applies the CTR Estimate and GLMix Ranking Model methods that are able to provide job vacancies recommendations to users based on the qualifications and majors of the user. The website also provides a facility to test the CTR Estimate and GLMix Ranking Model methods.