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Blog Intelligence  

Kim, Jae-Kyeong (경희대학교 경영대학 경영학과)
Kim, Hyea-Kyeong (경희대학교 경영대학 경영학과)
O, Hyouk ((유)삼일회계법인)
Publication Information
Journal of Information Technology Services / v.7, no.3, 2008 , pp. 71-85 More about this Journal
Abstract
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.
Keywords
Blog; Virtual Community; Collaborative Filtering; Interest Value;
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