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Content Based Recommendation and Summarization in the Blogosphere

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Executive Summary

This paper presents a stochastic graph based method for recommending or selecting a small subset of blogs that best represents a much larger set within a certain topic. Each blog is assigned a score that reflects how representative it is. Blog scores are calculated recursively in terms of the scores of their neighbors in a lexical similarity graph. A random walk is performed on a graph where nodes represent blogs and edges link lexically similar blogs. Lexical similarity is measured using either the cosine similarity measure, or the Kullback-Leibler (KL) divergence.

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