DOI QR코드

DOI QR Code

컨텐츠 유사도와 사회적 친화도 분석 기법을 혼합한 가치정보의 추천 시스템

Hybrid Recommendation System of Qualitative Information Based on Content Similarity and Social Affinity Analysis

  • 투고 : 2016.05.24
  • 심사 : 2016.08.20
  • 발행 : 2016.11.15

초록

추천 시스템은 개인에게 고도로 개인화된 아이템을 제공함으로써 아이템의 선택과 소비과정에서 발생하는 과부하를 줄여주고 효율성을 증대시키는 중요한 역할을 한다. 본 연구에서는 전통적인 추천 기법인 Content-Based(CB)기법과 최근 대두되는 Social Network-based(SN)기법을 접목하여 새로운 복합방식의 정보 추천 알고리즘을 제시한다. CB기법의 대표적인 한계점인 cold start problem과 SN기법에서 부족할 수 있는 추천 아이템의 전문성 문제를 상호 보완하는 형태가 되며, 특히 최근 소셜 네트워크의 특징인 비신뢰(non-trust) 기반의 영향력 있는 정보 확산자가 존재하는 환경에서 기법을 적용할 수 있도록 하였다. 또한 대부분 사람 추천 중심인 기존의 SN기법들과는 달리 사람에게 제공할 정보를 추천하는데 초점을 두며, 정보의 선정과정에서 개인의 소셜 네트워크와 실세계(real world)에서의 사회활동 정보를 모두 활용하여 더욱 더 개인화된 가치정보를 제공하고자 한다.

Recommendation systems play a significant role in providing personalized information to users, with enhanced satisfaction and reduced information overload. Since the mid-1990s, many studies have been conducted on recommendation systems, but few have examined the recommendations of information from people in the online social networking environment. In this paper, we present a hybrid recommendation method that combines both the traditional system of content-based techniques to improve specialization, and the recently developed system of social network-based techniques to best overcome a few limitations of the traditional techniques, such as the cold-start problem. By suggesting a state-of-the-art method, this research will help users in online social networks view more personalized information with less effort than before.

키워드

과제정보

연구 과제 주관 기관 : 한국연구재단

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