Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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The Effect of the Personalized Settings for CF-Based Recommender Systems

CF 기반 추천시스템에서 개인화된 세팅의 효과

  • Im, Il (School of Business, Yonsei University) ;
  • Kim, Byung-Ho (School of Business, Yonsei University)
  • 임일 (연세대학교 경영대학) ;
  • 김병호 (연세대학교 경영대학)
  • Received : 2012.06.09
  • Accepted : 2012.06.19
  • Published : 2012.06.30
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Abstract

In this paper, we propose a new method for collaborative filtering (CF)-based recommender systems. Traditional CF-based recommendation algorithms have applied constant settings such as a reference group (neighborhood) size and a significance level to all users. In this paper we develop a new method that identifies optimal personalized settings for each user and applies them to generating recommendations for individual users. Personalized parameters are identified through iterative simulations with 'training' and 'verification' datasets. The method is compared with traditional 'constant settings' methods using Netflix data. The results show that the new method outperforms traditional, ordinary CF. Implications and future research directions are also discussed.

논문에서는 협업필터링(collaborative filtering : CF) 기반한 추천시스템의 정확도를 높일 수 있는 방법을 제안하고 그 효과를 분석한다. 일반적인 CF기반 추천시스템에서는 시스템 세팅(참조집단 크기, 유의도 수준 등)을 한 가지 정해서 모든 경우에 대해서 동일하게 적용한다. 본 논문에서는 개별 사용자의 특성에 따라 이러한 세팅을 최적화 해서 개별적으로 적용하는 방법을 개발하였다. 이런 개인화된 세팅의 효과를 측정하기 위해서 Netflix의 자료를 사용해서 일반적인 추천시스템과 추천 정확도를 비교하였다. 분석 결과, 동일한 세팅을 적용하는 일반적인 추천시스템에 비해서 개인화된 세팅을 적용한 경우 정확도가 월등히 향상됨을 확인하였다. 이 결과의 시사점과 함께 미래 연구의 방향에 대해서도 논의한다.

Keywords

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