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dc.contributor.authorThelwall, Mike
dc.date.accessioned2006-06-20T14:51:54Z
dc.date.available2006-06-20T14:51:54Z
dc.date.issued2003
dc.identifier.citationJournal of Documentation, 59(2): 205-217
dc.identifier.issn00220418,00000000
dc.identifier.doi10.1108/00220410310463491
dc.identifier.urihttp://hdl.handle.net/2436/3139
dc.descriptionMain article
dc.description.abstractGoogle's PageRank is an influential algorithm that uses a model of Web use that is dominated by its link structure in order to rank pages by their estimated value to the Web community. This paper reports on the outcome of applying the algorithm to the Web sites of three national university systems in order to test whether it is capable of identifying the most important Web pages. The results are also compared with simple inlink counts. It was discovered that the highest inlinked pages do not always have the highest PageRank, indicating that the two metrics are genuinely different, even for the top pages. More significantly, however, internal links dominated external links for the high ranks in either method and superficial reasons accounted for high scores in both cases. It is concluded that PageRank is not useful for identifying the top pages in a site and that it must be combined with a powerful text matching techniques in order to get the quality of information retrieval results provided by Google.
dc.format.extent274740 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherMCB UP Ltd
dc.relation.urlhttp://www.emeraldinsight.com/10.1108/00220410310463491
dc.subjectAlgorithms
dc.subjectEffectiveness
dc.subjectInformation retrieval
dc.subjectUniversities
dc.subjectInternet
dc.titleCan Google's PageRank be used to find the most important academic Web pages?
dc.typeJournal article
dc.format.digYES
refterms.dateFOA2018-08-21T11:04:47Z
html.description.abstractGoogle's PageRank is an influential algorithm that uses a model of Web use that is dominated by its link structure in order to rank pages by their estimated value to the Web community. This paper reports on the outcome of applying the algorithm to the Web sites of three national university systems in order to test whether it is capable of identifying the most important Web pages. The results are also compared with simple inlink counts. It was discovered that the highest inlinked pages do not always have the highest PageRank, indicating that the two metrics are genuinely different, even for the top pages. More significantly, however, internal links dominated external links for the high ranks in either method and superficial reasons accounted for high scores in both cases. It is concluded that PageRank is not useful for identifying the top pages in a site and that it must be combined with a powerful text matching techniques in order to get the quality of information retrieval results provided by Google.


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