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• 영문명: Blog Intelligence
• 발행기관: 한국IT서비스학회
• 저자명: 김재경(Jae Kyeong Kim) 김혜경(Hyea Kyeong Kim) 오혁(Hyouk O)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제7권 제3호, 71~85쪽, 전체 15쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2008.09.01

영문 초록

  The rapid growth of blog has caused information overload where bloggers in the virtual community space are no longer able to effectively choose the blogs they are exposed to. Recommender systems have been widely advocated as a way of coping with the problem of information overload in e-business environment. Collaborative Filtering (CF) is the most successful recommendation method to date and used in many of the recommender systems. In this research, we propose a CF-based recommender system for bloggers to find their similar bloggers or preferable virtual community without burdensome search effort. For such a purpose, we apply the “Interest Value” to CF recommender systems. The Interest Value is the quantity value about users’ transaction data in virtual community, and can measure the opinion of users accurately. Based on the Interest Value, the neighborhood group is generated, and virtual community list is recommended using the Community Likeness Score (ClS). Our experimental results upon real data of Korean Blog site show that the methodology is capable of dealing with the information overload issue in virtual community space. And Interest Value is proved to have the potential to meet the challenge of recommendation methodologies in virtual community space.

목차

Abstract
1. 서론
2. 관련연구
3. 커뮤니티 추천시스템
4. 실험 및 평가
5. 결론 및 토론
참고문헌
저자소개

키워드

Blog Virtual Community Collaborative Filtering Interest Value