The Journal of Society for e-Business Studies (한국전자거래학회지)

Society for e-Business Studies (한국전자거래학회)
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Understanding the Performance of Collaborative Filtering Recommendation through Social Network Analysis

소셜네트워크 분석을 통한 협업필터링 추천 성과의 이해

  • 안성만 (국민대학교 경영대학 경영학부) ;
  • 김인환 (GS SHOP IT 연구개발팀) ;
  • 최병구 (국민대학교 경영대학 경영정보학부) ;
  • 조윤호 (국민대학교 경영대학 경영정보학부) ;
  • 김은홍 (국민대학교 경영대학 경영정보학부) ;
  • 김명균 (국민대학교 경영대학 경영학부)
  • Received : 2012.05.03
  • Accepted : 2012.05.21
  • Published : 2012.05.31
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Abstract

Collaborative filtering (CF), one of the most successful recommendation techniques, has been used in a number of different applications such as recommending web pages, movies, music, articles and products. One of the critical issues in CF is why recommendation performances are different depending on application domains. However, prior literatures have focused on only data characteristics to explain the origin of the difference. Scant attentions have been paid to provide systematic explanation on the issue. To fill this research gap, this study attempts to systematically explain why recommendation performances are different using structural indexes of social network. For this purpose, we developed hypotheses regarding the relationships between structural indexes of social network and recommendation performance of collaboration filtering, and empirically tested them. Results of this study showed that density and inconclusiveness positively affected recommendation performance while clustering coefficient negatively affected it. This study can be used as stepping stone for understanding collaborative filtering recommendation performance. Furthermore, it might be helpful for managers to decide whether they adopt recommendation systems.

협업필터링(collaborative filtering) 추천은 효과적인 추천을 위해 가장 널리 활용되는 기법 가운데 하나로 다양한 분야에서 널리 활용되고 있다. 협업필터링 추천과 관련하여 주요 이슈 가운데 하나는 왜 적용 도메인에 따라 추천 성과 간에 차이가 다르게 나타나는가이다. 이러한 추천 성과 간의 차이가 발생하는 원인에 대해 많은 연구들은 데이터의 특성에만 주목할 뿐 체계적인 설명을 제시하지 못하고 있는 것도 사실이다. 이러한 기존 연구의 문제점을 해결하기 위해 본 연구는 소셜네트워크의 구조적 측정 지표를 활용하여 추천 성과 간의 차이가 발생하는 원인을 보다 체계적으로 규명하고자 한다. 이를 위해 소셜네트워크의 구조적 측정지표와 협업필터링 추천 성과 간의 관계에 대한 가설을 수립하고 국내 H백화점의 거래데이터를 활용하여 이를 실증적으로 검증하였다. 검증 결과 밀도와 포괄성은 추천 성과에 긍정적인 영향을 미치는 반면 군집화계수는 부정적인 영향을 미치는 것을 파악하였다. 본 연구는 협업필터링 추천 성과를 이해할 수 있는 새로운 관점을 제시하였다. 또한 기업이 협업필터링 추천시스템을 도입하고자 할 때 그들의 의사결정에 도움을 줄 수 있는 가이드라인을 제시하였다는 점에서 그 의의가 있다.

Keywords

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