Quantification of Social Blog Network using B-Rank technique & Blog Recommandation
Abstract
Now a days, Among all communication services that Internet offers, one of the fastest growing are weblogs (abbreviated blogs) (Blood, 2000; Winer, 1999). blogging has become a common way for people to publish content on the Internet. Because blogs are easy to use, people can rapidly share their daily diaries, discuss the latest news, and express their opinions on numerous topics. Given this convenient platform, the number of blogs is increasing at a dramatic rate. Because blog growth is massive, blog readers can find numerous “hot blogs” on blog hosting sites, yet have no idea which ones contain the most informative content. Such as Browsing among so much information is no easy task, even more when blogs are often updated several times a day, and generic search engines do not update their indices so frequently. Another drawback of current search engines is that they perform only string searches; they not include any semantic information .Some blog service providers (BSPs) list hot blogs based on their number of visitors, but this indicator is weak and insufficient for determining blog popularity, which is a crucial issue in the massive blogosphere.
This project extracts real-world blog data and analyzes the interconnections within blog communities. Such interconnections reveal blogger behaviors and visibilities, as well as blog popularity — all of which might relate to blog quality. Social blog network model on this interconnection structure between blogs and, on this basis, developed a popularity ranking method, called BRank.
While conducting several experiments to analyze various explicit and implicit interconnection structures and discover variability in the impact of interactions in different communities should be made.
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