한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제16권2호
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  • Pages.156-163
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  • 2006
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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새로 출시되는 품목들을 위한 단어 기반의 사용자 선호도 예측 기법

A Prediction System of User Preferences for Newly Released Items Based on Words

  • 최윤석 (서울대학교 컴퓨터공학부) ;
  • 문병로 (서울대학교 컴퓨터공학부)
  • 발행 : 2006.04.01
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초록

협동적 여과(CF) 시스템은 구현의 용이성과 뛰어난 성능으로 널리 활용되고 있다. 그러나 이 시스템은 데이터 희소성 신상품 추천 불가, 추천 근거에 대한 설명 부족 등의 문제점을 포함하고 있어 이를 해결하기 위한 많은 연구가 진행되었다. 데이터 희소성 문제는 데이터의 누적에 따라 해결될 수 있지만, 협동적 여과 기법의 특성상 새로이 출시되는 품목에 대한 추천이 불가능하다. 이를 해결하기 위해 내용 기반(CB) 기법을 같이 사용하는 연구들이 제안되었다. 또한 협동적 여과 시스템은 추천 과정에 있어 추천 근거에 대한 설명을 제공하지 않는다. 본 연구에서는 추천에 대한 설명 기능을 포함하고 있는 선호 단어를 활용한 내용기반 예측 시스템을 제안한다. 이 시스템은 새로이 출시되는 영화에 대해 사용자의 영화에 대한 평가 정보를 예측하며, 추천의 근거가 되는 선호 단어를 제시한다. 또한 기존의 내용기반 예측 시스템에서 일어나는 속성 비매칭 문제로 인한 성능 저하를 막기 위해 기호 네트워크를 활용한 성능 개선 방법을 제안한다. 성능 비교를 위해 EachMovie 데이터베이스와 IMDb 사의 영화 홍보 데이터를 사용하였다.

CF systems are widely used in recommendation due to the easy implementation and the outstanding performance. They have several problems such as the sparsity problem, the first-rater problem, and recommending explanation. Many studies are suggested to resolve these problems. While the influence of the sparsity problem lessens as the users' data are accumulated, but the first-rater problem is originated from the CF systems and there are a number of researches to overcome the disadvantages of CF systems based on the content-based methods. Also CF systems are black boxes, providing no explanation of working of the recommendation. In this paper we present a content-based prediction system based on the preference words, which exposes the reasoning behind a recommendation. Our system predicts user's rating of a new movie and we suggest a semiotic network-based method to solve the mismatching problem between the items. For experimental comparison, we used EachMovie and IMDb dataset.

키워드

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