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A Movie Recommendation Method Using Rating Difference Between Items

항목 간 선호도 차이를 이용한 영화 추천 방법

  • Oh, Se-Chang (Department of Information & Communication, Sejong Cyber University) ;
  • Choi, Min (Department of Information and Communication Engineering, Chungbuk National University)
  • Received : 2013.09.17
  • Accepted : 2013.10.21
  • Published : 2013.11.30

Abstract

User-based and item-based method have been developed as the solutions of the movie recommendation problem. However, these methods are faced with the sparsity problem and the problem of not reflecting user's rating respectively. In order to solve these problems, there is a research on the combination of the two methods using the concept of similarity. In reality, it is not free from the problem of sparsity, since it has a lot of parameters to be calculated. In this study, we propose a recommendation method using rating difference between items in order to complement this problem. This method is relatively free from the problem of sparsity, since it has less parameters to be calculated. And it can get more accurate results by reflecting the users rating to calculate the parameters. In experiments for the proposed method, the initial error is large, but the performance has been quickly stabilized after. In addition, it showed a 0.0538 lower average error compared to the existing method using similarity.

영화 추천 문제에 대한 해법으로 사용자 기반 추천 방법과 항목 기반 추천 방법이 연구되어왔다. 그러나 이들은 각각 희박성의 문제와 사용자의 선호도를 반영하지 못한다는 문제를 안고 있다. 이러한 문제들을 해결하기 위해서 유사도의 개념을 이용해 두 가지 방법을 조합하는 연구가 있으나 계산해야 할 파라메타 수가 많아 현실적으로 희박성의 문제에서 자유롭지 못하다. 본 연구에서는 이러한 문제를 보완하기 위하여 항목 간 선호도 차이를 이용한 추천 방법을 제안한다. 이 방법은 계산해야 할 파라메타 수가 적어 희박성의 문제에서 비교적 자유롭다. 또한 파라메타 계산에 사용자들이 평가한 선호도를 반영함으로써 보다 정확한 결과를 얻을 수 있다. 실험 결과 제안된 방법은 초기에는 오류가 크지만 빠르게 성능이 안정화되는 것을 보여준다. 또한 유사도를 이용한 기존의 추천 방법과 비교하여 평균 오류를 0.0538 낮추는 결과를 보였다.

Keywords

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