An Analytical Comparison of Approaches to Personalizing PageRank
作者： Taher Haveliwala , Sepandar Kamvar , Glen Jeh
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摘要: PageRank, the popular link-analysis algorithm for ranking web pages, assigns a query and user independent estimate of "importance" to pages. Query sensitive extensions which use basis set biased PageRank vectors, have been proposed in order personalize function tractable way. We analytically compare three recent approaches personalizing discuss tradeoffs each one.
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