LEADER 04548nam 2200757Ia 450 001 9910460270503321 005 20200520144314.0 010 $a1-282-66584-7 010 $a9786612665844 010 $a1-4008-3706-5 024 7 $a10.1515/9781400837069 035 $a(CKB)2670000000040755 035 $a(EBL)557150 035 $a(OCoLC)650641334 035 $a(SSID)ssj0000424304 035 $a(PQKBManifestationID)11306069 035 $a(PQKBTitleCode)TC0000424304 035 $a(PQKBWorkID)10471594 035 $a(PQKB)10965752 035 $a(MiAaPQ)EBC557150 035 $a(WaSeSS)Ind00024399 035 $a(DE-B1597)446585 035 $a(OCoLC)979579421 035 $a(DE-B1597)9781400837069 035 $a(PPN)170258580 035 $a(Au-PeEL)EBL557150 035 $a(CaPaEBR)ebr10402717 035 $a(CaONFJC)MIL266584 035 $a(EXLCZ)992670000000040755 100 $a20100428d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aNumerical algorithms for personalized search in self-organizing information networks$b[electronic resource] /$fSep Kamvar 205 $aCourse Book 210 $aPrinceton, N.J. $cPrinceton University Press$d2010 215 $a1 online resource (295 p.) 300 $aDescription based upon print version of record. 311 $a0-691-14503-2 320 $aIncludes bibliographical references. 327 $t Frontmatter -- $tContents -- $tTables -- $tFigures -- $tAcknowledgments -- $tChapter One. Introduction -- $tPART I. World Wide Web -- $tChapter Two. PageRank -- $tChapter Three. The Second Eigenvalue of the Google Matrix -- $tChapter Four. The Condition Number of the PageRank Problem -- $tChapter Five. Extrapolation Algorithms -- $tChapter Six. Adaptive PageRank -- $tChapter Seven. BlockRank -- $tPART II. P2P Networks -- $tChapter Eight. Query-Cycle Simulator -- $tChapter Nine. Eigen Trust -- $tChapter Ten. Adaptive P2P Topologies -- $tChapter Eleven. Conclusion -- $tBibliography 330 $aThis book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. Representing much of the foundational research in this field, the book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data. Sep Kamvar focuses on eigenvector-based techniques in Web search, introducing a personalized variant of Google's PageRank algorithm, and he outlines algorithms--such as the now-famous quadratic extrapolation technique--that speed up computation, making personalized PageRank feasible. Kamvar suggests that Power Method-related techniques ultimately should be the basis for improving the PageRank algorithm, and he presents algorithms that exploit the convergence behavior of individual components of the PageRank vector. Kamvar then extends the ideas of reputation management and personalized search to distributed networks like peer-to-peer and social networks. He highlights locality and computational considerations related to the structure of the network, and considers such unique issues as malicious peers. He describes the EigenTrust algorithm and applies various PageRank concepts to P2P settings. Discussion chapters summarizing results conclude the book's two main sections. Clear and thorough, this book provides an authoritative look at central innovations in search for all of those interested in the subject. 606 $aDatabase searching$xMathematics 606 $aInformation networks$xMathematics 606 $aContent analysis (Communication)$xMathematics 606 $aSelf-organizing systems$xData processing 606 $aAlgorithms 606 $aInternet searching$xMathematics 608 $aElectronic books. 615 0$aDatabase searching$xMathematics. 615 0$aInformation networks$xMathematics. 615 0$aContent analysis (Communication)$xMathematics. 615 0$aSelf-organizing systems$xData processing. 615 0$aAlgorithms. 615 0$aInternet searching$xMathematics. 676 $a025.5/24 700 $aKamvar$b Sep$f1977-$01033777 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910460270503321 996 $aNumerical algorithms for personalized search in self-organizing information networks$92452499 997 $aUNINA