Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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An Agent-based Approach for Distributed Collaborative Filtering

분산 협력 필터링에 대한 에이전트 기반 접근 방법

  • 김병만 (금오공과대학교 컴퓨터공학부) ;
  • 이경 (한국정보통신대학교 공학부) ;
  • ;
  • 여동규 (금오공과대학교 컴퓨터공학부)
  • Published : 2006.11.15
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Abstract

Due to the usefulness of the collaborative filtering, it has been widely used in both the research and commercial field. However, there are still some challenges for it to be more efficient, especially the scalability problem, the sparsity problem and the cold start problem. In this paper. we address these problems and provide a novel distributed approach based on agents collaboration for the problems. We have tried to solve the scalability problem by making each agent save its users ratings and broadcast them to the users friends so that only friends ratings and his own ratings are kept in an agents local database. To reduce quality degradation of recommendation caused by the lack of rating data, we introduce a method using friends opinions instead of real rating data when they are not available. We also suggest a collaborative filtering algorithm based on user profile to provide new users with recommendation service. Experiments show that our suggested approach is helpful to the new user problem as well as is more scalable than traditional centralized CF filtering systems and alleviate the sparsity problem.

협력 필털링은 그 유용성으로 인해 현재 학문적으로나 상업적으로 널리 사용되고 있지만 확장성 문제, 평가 데이타의 희박성 문제, 초기 평가 문제 둥을 안고 있다. 본 논문에서는 이러한 문제들을 일부 해결하기 위해 에이전트 간 협력에 기초한 분산 협력필터링 방법을 제안하였다. 제안 방법에서는 사용자의 평가정보를 에이전트가 지역 데이타베이스에 보관하고 이 정보를 친구들에게만 전파하는 방법을 사용함으로써 사용자 증가에 따른 확장성 문제를 해결하고자 하였다. 그리고 평가 데이타 부족에 따른 추천질 저하를 줄이기 위해 친구 에이전트의 의견을 반영하는 방법을 사용하였고 새로운 사용자에 대해서도 추천이 가능토록 하기 위해 사용자 프로파일을 이용한 협력필터링 방법을 사용하였다. 실험결과, 본 제안 방법이 확장성뿐만 아니라 데이타 희박성 문제 및 새로운 사용자 문제에도 도움이 됨을 확인할 수 있었다.

Keywords

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