google’s pagerank by zack kenz. outline intro to web searching review of linear algebra weather...
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Google’s PageRank
By Zack Kenz
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Outline
Intro to web searchingReview of Linear AlgebraWeather exampleBasics of PageRankSolving the Google MatrixCalculating the PageRankWrapping up
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Some Search Engine History
Early basis of searching was on page content only
Bonuses for word placementPaying for placementNatural language searches (Think: Ask
Jeeves)Meta search engines
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Why Google?
No one exploited the link structure of the internet
Relatively easy to exploit content-based engines with concealed text
Adaptive to a growing internet
Simpler, faster
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PageRank, According to Google
“PageRank relies on the uniquely democratic nature of the web by using its vast link structure as an indicator of an individual page's value. In essence, Google interprets a link from page A to page B as a vote, by page A, for page B.”
“Google looks at considerably more than the sheer volume of votes, or links a page receives; for example, it also analyzes the page that casts the vote. Votes cast by pages that are themselves ‘important’ weigh more heavily and help to make other pages ‘important.’”
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Linear Algebra Terms
Row Stochastic MatrixEigenvector: A nonzero vector x such
that Ax=λx for a scalar λEigenvalue: A scalar λ that gives a
nontrivial solution x for Ax=λxDominant Eigenvalue (eigenvector)
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Tomorrow’s Weather
Example on the board
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Scoring Web Pages
Random web surfer
Goal: Assign a score to over 25 billion web pages, store the scores
Score based on the probability of going to a particular page
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Surf’s Up!
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Hyperlink Matrix
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Hyperlink Matrix
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Dangling Nodes
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Dangling Nodes
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Dangling Nodes
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Web Link Surfer Matrix
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One More Fix
Need to account for the fact that a surfer can type in URLs instead of using links
Add in a personalization vector, When multiplied by a column vector of ones, we get an
additional personalization matrix
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One More Fix
Need to account for the fact that a surfer can type in URLs instead of using links
Add in a personalization vector, When multiplied by a column vector of ones, we get an
additional personalization matrix
![Page 18: Google’s PageRank By Zack Kenz. Outline Intro to web searching Review of Linear Algebra Weather example Basics of PageRank Solving the Google Matrix Calculating](https://reader036.vdocument.in/reader036/viewer/2022062313/56649cff5503460f949d03e0/html5/thumbnails/18.jpg)
Google Matrix
Recall
is a damping factor, usually .85
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The True Google Matrix?
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Solution of the Google Matrix
Since the Google matrix is row stochastic, it has an eigenvalue of λ=1
λ=1 is biggest and not repeated Let be the corresponding eigenvector The eigensystem has a unique
solution for , then, is a row probability vector
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Solution of the Google Matrix
Since the Google matrix is row stochastic, it has an eigenvalue of λ=1
λ=1 is biggest and not repeated Let be the corresponding eigenvector The eigensystem has a unique
solution for , then, is a row probability vector
contains every page’s PageRank
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Computing Scores:The Linear Algebra Way
Recall
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λ = 1 is the dominant eigenvalue of G and is the dominant left eigenvector
As a result the power method applied to G converges to the PageRank vector
Given a starting vector like , the power method calculates successive iterates until a stopping condition is reached
Computing Scores:The Power Method
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Speeding Things Up
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Wrapping Up:The Overall Page Scoring
PageRank is still only a portion of what determines the order of search results
Results are based off of many factors, especially page content
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Wrapping Up:Improving PageRank
Avoiding link spamming – tweak the personalization vector and α
Power method convergence algorithmsDummy node
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Questions?
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Questions?
Rebecca S Wills. Google’s PageRank: The Math Behind the Search Engine. Department of Mathematics, North Carolina State University. 1 May 2006.
Amy N. Langville and Carl D. Meyer. Fiddling with PageRank. Department of Mathematics, North Carolina State University. 15 August 2003
http://www.searchenginehistory.com/ http://www.google.com/technology/ and http://www.google.com David C Lay. Linear Algebra and Its Applications, 3ed. Pearson Education:
2003. Dr. Biebighauser
http://eperformance.co.uk/uploaded_images/google%20beta-786468.jpg http://webmechanics.uoregon.edu/Images/Surf%20web.jpg http://www.modmyifone.com/iphone_wallpapers/file.php?n=282&w=l http://www.smashingmagazine.com/images/pagerank/google-pagerank.jpg
Sources
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