The Journal of Korean Institute of Information Technology (한국정보기술학회논문지)

Korean Institute of Information Technology (한국정보기술학회)
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Keyword-based Recommender System Dataset Construction and Analysis

키워드 기반 추천시스템 데이터 셋 구축 및 분석

  • 배은영 (숙명여자대학교 소프트웨어학부) ;
  • 유석종 (숙명여자대학교 소프트웨어학부)
  • Received : 2018.03.09
  • Accepted : 2018.06.22
  • Published : 2018.06.30
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Abstract

Recommender system is a system that recommends something that a person would like. Recommender systems are widely used and evolving in various fields such as Amazon product recommendation, Facebook or Linkedin friend recommendation, Netflix and Watcha movie recommendation, behavior based advertisement, news recommendation. In the academia, interest and research on recommender systems are steadily increasing, and the number of papers in this field is increasing year by year, and the field of research is becoming more and more fragmented and diversified. In this paper, we constructed a dataset based on dissertation data of recommender system obtained from IEEE Xplore Digital Library and ACM Digital Library, and proceeded with the analysis in order to grasp major research topics, application domains, preference techniques, and trends in research topics.

추천시스템(Recommender System)이란 대상자가 좋아할 만한 무언가를 추천하는 시스템을 일컫는다. 아마존의 상품 추천, 페이스북이나 링크드인의 친구 추천, 넷플릭스와 왓차의 영화 추천, 행태 기반 광고, 뉴스 추천 등 여러 분야에서 추천시스템은 이미 널리 활용 중이며 진화 중에 있다. 학계에서도 추천시스템에 대한 관심과 연구는 꾸준하게 증가를 하고 있으며, 이 분야의 논문 수 또한 해마다 증가하고 있고, 연구 분야 또한 점점 세분화되고 다양해지고 있다. 본 논문에서는 추천시스템에 관한 주요 연구 주제나 적용 대상 도메인, 선호기법, 연구 주제에 대한 트렌드 등을 파악하기 위하여 IEEE Xplore 전자 도서관 및 ACM 전자 도서관으로부터 얻은 추천시스템 논문 관련 자료를 토대로 데이터 셋을 구축하고 분석을 진행하였다.

Keywords

References

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