The Journal of Korean Institute of Information Technology (한국정보기술학회논문지)

Korean Institute of Information Technology (한국정보기술학회)
권호 표지

Collaborative Filtering Techniques Using Social Network Analysis for UCC Recommendation

UCC 추천을 위한 사회 네트워크 분석을 활용한 협업 필터링 기법 연구

  • 정중희 (한양대학교 경영학과) ;
  • 김종우 (한양대학교 경영대학 경영학부)
  • Published : 2013.01.31
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Abstract

In this study, a collaborative filtering (CF) based recommendation method which uses social network analysis measures is proposed and tested empirically to improve the performance of recommendation. Current CF techniques recommend appropriate product information to customers based on users' preference similarity. The proposed method uses centrality measures which can be obtained from user social network with user' preference similarity to select appropriate product information for users. The recommendation performance of the proposed method is compared with that of current CF empirically. To test performance empirically, user visiting web log data of www.youtube.com which is a representative UCC (User Created Contents) site is used. The experimental results show that the combined usage of social network analysis measures can contribute to improve recommendation performance.

본 논문에서는 사회 네트워크 분석(Social Network Analysis)의 척도를 협업필터링(Collaborative Filtering) 기법에 접목하여 추천 성과를 향상시킬 수 있는 방안을 제안하고 비교 검증하였다. 기존 협업필터링 기법은 고객 간의 선호도 유사성을 바탕으로 고객에게 적합한 상품 정보를 제공한다. 제시한 방법은 고객들의 선호도 유사성과 함께, 이 유사성을 기초로 구성된 고객 간의 네트워크 척도인 중심성을 활용하여 고객에서 적합한 상품 정보를 선정한다. 제시한 방법의 추천 성과를 기존 협업필터링의 추천 성과와 실증적으로 비교하였다. 실증적인 검증을 위해서 대표적 UCC (User Created Contents) 사이트인 www.youtube.com에 방문한 웹 로그 데이터를 활용하였으며, 실험 결과 사회 네트워크 분석 척도의 활용을 통해서 추천 성과를 향상할 수 있음을 확인하였다.

Keywords

References

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