• 제목/요약/키워드: Job recommendation

검색결과 6건 처리시간 0.045초

인재매칭을 위한 내용기반 척도학습모형의 설계 (A Design of Content-based Metric Learning Model for HR Matching)

  • 송희석
    • Journal of Information Technology Applications and Management
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    • 제27권6호
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    • pp.141-151
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    • 2020
  • The job mismatch between job seekers and SMEs is becoming more and more intensifying with the serious difficulties in youth employment. In this study, a bi-directional content-based metric learning model is proposed to recommend suitable jobs for job seekers and suitable job seekers for SMEs, respectively. The proposed model not only enables bi-directional recommendation, but also enables HR matching without relearning for new job seekers and new job offers. As a result of the experiment, the proposed model showed superior performance in terms of precision, recall, and f1 than the existing collaborative filtering model named NCF+GMF. The proposed model is also confirmed that it is an evolutionary model that improves performance as training data increases.

양방향 인재매칭을 위한 BERT 기반의 전이학습 모델 (A BERT-based Transfer Learning Model for Bidirectional HR Matching)

  • 오소진;장문경;송희석
    • Journal of Information Technology Applications and Management
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    • 제28권4호
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    • pp.33-43
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    • 2021
  • While youth unemployment has recorded the lowest level since the global COVID-19 pandemic, SMEs(small and medium sized enterprises) are still struggling to fill vacancies. It is difficult for SMEs to find good candidates as well as for job seekers to find appropriate job offers due to information mismatch. To overcome information mismatch, this study proposes the fine-turning model for bidirectional HR matching based on a pre-learning language model called BERT(Bidirectional Encoder Representations from Transformers). The proposed model is capable to recommend job openings suitable for the applicant, or applicants appropriate for the job through sufficient pre-learning of terms including technical jargons. The results of the experiment demonstrate the superior performance of our model in terms of precision, recall, and f1-score compared to the existing content-based metric learning model. This study provides insights for developing practical models for job recommendations and offers suggestions for future research.

이주자 생활대책 제도의 문제점과 개선방안에 관한 연구 - SH공사 문정지구를 중심으로 - (A Study on Problems and Improvement of Living Measure System - Focused on SH'Moonjung Project -)

  • 박용한
    • 한국전자통신학회논문지
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    • 제7권6호
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    • pp.1505-1511
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    • 2012
  • 우리나라는 급격한 경제성장으로 주택, 공장, 공공용지 등 대량의 토지수요가 필요하게 되었으며 이에 따라 손실보상도 재산권보상에서 생활권보상으로 변화되어왔다. 본 논문은 실무상 생활대책제도의 문제점을 살펴보고 개선방안을 제시하고자 한다. 개선방안으로 첫째 토지보상법상 생활대책제도의 규정화, 둘째 양봉업과 같은 보상에 대한 요건 강화, 셋째 우선고용이나 취업알선, 직업훈련 등의 필요성을 제시하였다. 이러한 생활대책은 공영개발사업을 원활하게 할 수 있을 것이다.

Proactive: Comprehensive Access to Job Information

  • Lee, Danielle;Brusilovsky, Peter
    • Journal of Information Processing Systems
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    • 제8권4호
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    • pp.721-738
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    • 2012
  • The Internet has become an increasingly important source for finding the right employees, so more and more companies post their job openings on the Web. The large amount and dynamic nature of career recruiting information causes information overload problems for job seekers. To assist Internet users in searching for the right job, a range of research and commercial systems were developed over the past 10 years. Surprisingly, the majority of existing job search systems support just one, rarely two ways of information access. In contrast, our work focused on exploring a value of comprehensive access to job information in a single system (i.e., a system which supports multiple ways). We designed Proactive, a recommendation system providing comprehensive and personalized information access. To assist the varied needs of users, Proactive has four information retrieval methods - a navigable list of jobs, keyword-based search, implicit preference-based recommendations, and explicit preference-based recommendations. This paper introduces the Proactive and reports the results of a study focusing on the experimental evaluation of these methods. The goal of the study was to assess whether all of the methods are necessary for users to find relevant jobs and to what extent different methods can meet different users' information requirements.

사용자의 선호도 정보를 활용한 직무 추천 시스템 연구 (A Study on the Job Recommender System Using User Preference Information)

  • 이청용;전상홍;이창재;김재경
    • 한국IT서비스학회지
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    • 제20권3호
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    • pp.57-73
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    • 2021
  • 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.

중장년층 일자리 요구사항 분석 및 인력 고용 매칭 시스템 개발 (Job Preference Analysis and Job Matching System Development for the Middle Aged Class)

  • 김성찬;장진철;김성중;진효진;이문용
    • 지능정보연구
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    • 제22권4호
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    • pp.247-264
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    • 2016
  • 저출산 및 인구 고령화가 가속화되면서, 중장년 퇴직자 등 노동 소외 계층의 취업난 해결은 우리 사회의 핵심 과제로 등장하고 있다. 온라인에는 수많은 일자리 요구 정보가 산재해 있으나, 이를 중장년 구직자에게 제대로 매칭시키지는 못하고 있다. 워크넷 취업 로그에 따르면 구직자가 선호하는 직종에 취업하는 경우는 약 24%에 불과하다. 그러므로, 이러한 문제를 극복하기 위해서는 구직자에게 일자리 정보를 매칭시킬 때 선호하는 직종과 유사한 직종들을 추천하는 소프트 매칭 기법이 필수적이다. 본 연구는 중장년층에 특화된 소프트 직업 매칭 알고리즘과 서비스를 고안하고 개발하여 제공하는 것을 목표로 한다. 이를 위하여 본 연구에서는 1) 대용량의 구직 활동 기록인 워크넷 로그로부터 중장년층의 일자리 특성 및 요구 추세를 분석하였다. 2) 중장년층의 일자리 추천을 위해 직종 유사도 기준으로 일자리 분류표(KOCM)를 재정렬하였다. 이 결과를 이용하여, 3) 중장년에 특화된 인력 고용 소프트 매칭 직업 추천 알고리즘(MOMA)을 개발하여 구인 구직 웹사이트에 적용하였다. 자체 저작한 중장년층 특화 일자리 분류표(KOCM)를 이용한 소프트 일자리 매칭 시스템의 정확도를 측정하였을 때, 실제 고용 결과 기준, 하드 매칭 대비 약 20여 배의 성능 향상을 보였다. 본 연구내용을 적용하여 개발한 중장년층 특화 구직 사이트는 중장년층의 구직 과정에서 입력 정보 부담을 최소화하고 소프트 매칭을 통해 사용자의 요구직종에 적합한 일자리를 정확하고 폭넓게 추천함으로 중장년층의 삶의 질 향상에 기여할 수 있을 것으로 기대된다.