Blogs have influenced our life profoundly and people preferred to subscribe to influential bloggers they are interested in. Hence, the identification of influential bloggers automatically has become an important task. Previous researches focus on the blog sites in which there exist abundant hyperlinks, but their methods can not scale to ones that have few hyperlinks. In this paper, we propose a novel algorithm called SimRank to recommend influential bloggers. Our algorithm is based on the observation that the reproduction of blog posts and similar contents is common in blogosphere, which form implicit links between bloggers. By measuring the text similarity of the blog posts, we create link graph between bloggers, and adopt the PageRank algorithm to rank the importance of bloggers. Experimental results indicate that our proposed algorithm is effective in recommending bloggers.
['C. Sun', 'Bingquan Liu', 'Chengjie Sun', 'Deyuan Zhang', 'Xiaolong Wang']
SimRank: A link analysis based blogger recommendation algorithm using text similarity