Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
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Social Category based Recommendation Method

소셜 카테고리를 이용한 추천 방법

  • Yoo, So-Yeop (Dept. of Software Design & Management, Gachon Univ.) ;
  • Jeong, Ok-Ran (Dept. of Software Design & Management, Gachon Univ.)
  • Received : 2014.08.21
  • Accepted : 2014.09.23
  • Published : 2014.10.31
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Abstract

SNS becomes a recent issue, and many researches in various kinds of field are being done by taking advantage of it. Especially, there are many researches existed on the system that finds user's interest and makes recommendation based on multiple social data generated on the SNS. User's interest is not only revealed from the user's writing but also from the user's relationship with friends. This study proposes a recommendation method that extracts user's interest by using social relationship and its categorization applies it to the recommendation. In this way, it can recommend user's interest with category based on the writings by the user and furthermore it can apply the user's relationship with his/her friends for more accurate recommendation. In addition, if necessary, the recommendation can be made by extracting any interest shared between the user and specific friends. Through experiments, we show that our method using social category can produce satisfactory result.

최근 SNS가 이슈가 되고 다양한 분야에서 이를 이용한 연구가 활발하게 진행되고 있다. 특히 SNS 상에서 생성되는 여러 소셜 데이터를 기반으로 사용자의 관심사를 찾아내고 추천해 주는 시스템에 대한 연구가 대두되고 있다. 사용자의 관심과 선호도는 단순히 사용자가 작성한 글에서만 나타나는 것이 아니라, 친구와의 관계와 작성한 내용기반으로 분류되는 카테고리를 이용하여 파악될 수 있다. 본 논문에서는 사용자의 사회적 관계와 사용자가 작성한 소셜 데이터의 카테고리를 이용하여 사용자의 선호도를 자동으로 추출하고, 이를 기반으로 추천하는 방법을 제안한다. 그리고 실험을 통해 제안한 기법의 유효성을 검증한다.

Keywords

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