@techreport{ilprints596,
          number = {2003-35},
           month = {June},
          author = {Taher Haveliwala and Sepandar Kamvar and Glen Jeh},
           title = {An Analytical Comparison of Approaches to Personalizing PageRank},
            type = {Technical Report},
       publisher = {Stanford},
     institution = {Stanford InfoLab},
            year = {2003},
             url = {http://ilpubs.stanford.edu:8090/596/},
        abstract = {PageRank, the popular link-analysis algorithm for ranking web pages, assigns a query and user independent estimate of "importance" to web pages.  Query and user sensitive extensions of PageRank, which use a basis set of biased PageRank vectors, have been proposed in order to personalize the ranking function in a tractable way.  We analytically compare three recent approaches to personalizing PageRank and discuss the tradeoffs of each one.}
}