The KIPS Transactions:PartD (정보처리학회논문지D)

Korea Information Processing Society (한국정보처리학회)
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A Refined Neighbor Selection Algorithm for Clustering-Based Collaborative Filtering

클러스터링기반 협동적필터링을 위한 정제된 이웃 선정 알고리즘

  • 김택헌 (연세대학교 컴퓨터과학과 BK21) ;
  • 양성봉 (연세대학교 컴퓨터과학과)
  • Published : 2007.06.30
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Abstract

It is not easy for the customers to search the valuable information on the goods among countless items available in the Internet. In order to save time and efforts in searching the goods the customers want, it is very important for a recommender system to have a capability to predict accurately customers' preferences. In this paper we present a refined neighbor selection algorithm for clustering based collaborative filtering in recommender systems. The algorithm exploits a graph approach and searches more efficiently for set of influential customers with respect to a given customer; it searches with concepts of weighted similarity and ranked clustering. The experimental results show that the recommender systems using the proposed method find the proper neighbors and give a good prediction quality.

전자상거래에서 취급되는 상품은 오프라인 상에서 뿐만 아니라 온라인 상에서도 그 종류가 매우 다양하고 수 또한 셀 수 없을 정도로 많다. 이런 이유로 고객들이 그들의 요구에 따른 가장 적합한 상품을 찾기란 쉬운 일이 아니다. 따라서 다양한 성향을 갖는 고객들에게 더 좋은 가치를 갖는 양질의 정보를 제공하기 위해서는 고객들의 선호도를 정확하게 예측하는 능력을 갖는 개인화된 추천 시스템의 개발이 필요하다. 본 논문에서는 추천 시스템에서 클러스터링을 기반으로 한 협동적 필터링을 위한 정제된 이웃선정 방법을 제안한다. 이 방법은 그래프 접근법을 이용하며, 고객에게 영향을 줄 수 있는 다른 고객들의 집합을 보다 효율적으로 찾아낸다. 제안한 방법은 또한 서열화된 클러스터링 및 유사 가중치를 이용하여 탐색을 수행하여 보다 유용한 이웃을 선정한다. 실험 결과는 본 논문에서 제안한 방법을 이용한 추천 시스템이 보다 유용한 이웃 고객들을 찾아냄으로써 추천 시스템의 예측의 질을 향상시켜 주는 것을 보여준다.

Keywords

References

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