응용통계연구 (The Korean Journal of Applied Statistics)

  • 제33권6호
  • /
  • Pages.833-842
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  • 2020
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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콘텐츠 속성에 따른 계층적 그룹화 추천시스템: 'The Movie Dataset' 분석사례연구

Hierarchical grouping recommendation system based on the attributes of contents: a case study of 'The Movie Dataset'

  • 김윤경 (숙명여자대학교 통계학과) ;
  • 여인권 (숙명여자대학교 통계학과)
  • Kim, Yoon Kyoung (Department of Statistics, Sookmyung Women's University) ;
  • Yeo, In-Kwon (Department of Statistics, Sookmyung Women's University)
  • 투고 : 2020.11.03
  • 심사 : 2020.11.18
  • 발행 : 2020.12.31
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⟨ 이전 논문 다음 논문 ⟩

초록

넷플릭스, 아마존, 유튜브 등 대형 플랫폼에서는 고객의 다양한 정보를 활용하여 정밀한 추천시스템을 마련하고 여기서 추천된 상당수의 아이템이 실제 구매로 이어지고 있다. 본 논문에서는 추천 컨텐츠의 속성에 따라 사용자의 선호도에 차이가 있을 것이라고 예상하고 콘텐츠의 속성에 따라 군집분석을 실시하였다. 속성의 형태와 관계없이 사용할 수 있도록 Gower 거리를 사용했다. 본 논문에서는 영화 평점 사이트인 'The Movie Dataset'의 자료를 이용하여 영화의 기본정보인 장르, 감독 및 배우 변수를 바탕으로 사용자를 계층적으로 분류하고 영화를 추천하였다. 본 논문에서 제안한 추천 시스템을 평가하기 위하여 각 사용자 그룹별로 훈련자료와 검증자료로 나누어 정밀도를 살펴보았다. 그 결과 UBCF보다 월등히 높은 정밀도를 갖는 것으로 나타났다.

Global platforms such as Netflix, Amazon, and YouTube have developed a precise recommendation system based on various information from large set of customers and many of the items recommended here are leading to actual purchases. In this paper, a cluster analysis was conducted according to the attribute of the content, expecting that there would be a difference in user preferences according to the attribute of the recommended content. Gower distance was used for use regardless of the type of variables. In this paper, using the data of movie rating site 'The Movie Dataset', the users were grouped hierarchically and recommended movies based on genre, director and actor variables. To evaluate the recommended systems proposed, user group was divided into train set and test set to examine the precision. The results showed that proposed algorithms have far higher precision than UBCF.

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참고문헌

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