A Study on the Job Recommender System Using User Preference Information

사용자의 선호도 정보를 활용한 직무 추천 시스템 연구

  • 이청용 (경희대학교 대학원 빅데이터응용학과) ;
  • 전상홍 (경희대학교 대학원 빅데이터응용학과) ;
  • 이창재 (경희대학교 대학원 경영학과) ;
  • 김재경 (경희대학교 경영대학/대학원 빅데이터응용학과)
  • Received : 2021.03.31
  • Accepted : 2021.06.14
  • Published : 2021.06.30


Recently, online job websites have been activated as unemployment problems have emerged as social problems and demand for job openings has increased. However, while the online job platform market is growing, users have difficulty choosing their jobs. When users apply for a job on online job websites, they check various information such as job contents and recruitment conditions to understand the details of the job. When users choose a job, they focus on various details related to the job rather than simply viewing and supporting the job title. However, existing online job websites usually recommend jobs using only quantitative preference information such as ratings. However, if recommendation services are provided using only quantitative information, the recommendation performance is constantly deteriorating. Therefore, job recommendation services should provide personalized services using various information about the job. This study proposes a recommended methodology that improves recommendation performance by elaborating on qualitative preference information, such as details about the job. To this end, this study performs a topic modeling analysis on the job content of the user profile. Also, we apply LDA techniques to explore topics from job content and extract qualitative preferences. Experiments show that the proposed recommendation methodology has better recommendation performance compared to the traditional recommendation methodology.



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