Numerical algorithms for personalized search in self-organizing information networks
This 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 underlyi...
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Princeton, N.J
Princeton University Press
2010
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| ISBN: | 9780691145037, 0691145032, 1400837065, 9781400837069 |
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| Abstract | This 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. |
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| AbstractList | This 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. This 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. This 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. No detailed description available for "Numerical Algorithms for Personalized Search in Self-organizing Information Networks". |
| Author | Kamvar, Sep |
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| Keywords | Cluster analysis Ranking (information retrieval) Personalization Reputation system Reputation management Robustness (computer science) Randomized algorithm Data corruption Naming convention (programming) Load balancing (computing) Sampling (statistics) Hash function Computer network Collaborative filtering Eigenvalue algorithm Eigenvalues and eigenvectors Variable (computer science) Peer-to-peer Algorithm design Deterministic algorithm CPU cache Parameter (computer programming) QR algorithm Data portability Power iteration Numerical linear algebra QR decomposition Online auction Matrix decomposition Algorithm Network simulation Extrapolation Proportionality (mathematics) Numerical analysis Computation Regularization (mathematics) Repository (version control) Selection algorithm Memory access pattern Patch (computing) Replication (computing) Sign (mathematics) Polynomial long division Characteristic polynomial Upload Iteration Computing Computer data storage Web search engine Personalized search Dynamic programming Rate of convergence Tree (data structure) PageRank Cryptographic hash function Data set Federated search Search algorithm Instance (computer science) Topic-Sensitive PageRank Variable (mathematics) Parallel computing File system Google matrix Machine learning Greedy algorithm EigenTrust Server (computing) Scientific notation Human–computer interaction (security) |
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| Notes | Bibliography: p. [135]-139 |
| OCLC | 650641334 |
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| Snippet | This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks.... No detailed description available for "Numerical Algorithms for Personalized Search in Self-organizing Information Networks". |
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| SubjectTerms | Algorithm Algorithm design Algorithms Characteristic polynomial Cluster analysis Collaborative filtering Computation Computer data storage Computer network COMPUTERS COMPUTERS / Computer Science COMPUTERS / Programming / Algorithms Computing Content analysis (Communication) Content analysis (Communication) -- Mathematics CPU cache Cryptographic hash function Data corruption Data portability Data processing Database searching Database searching -- Mathematics Deterministic algorithm Dynamic programming EigenTrust Eigenvalue algorithm Eigenvalues and eigenvectors Extrapolation Federated search File system Google matrix Greedy algorithm Hash function Human–computer interaction (security) Information networks Information networks -- Mathematics Instance (computer science) Internet searching Internet searching -- Mathematics Iteration Load balancing (computing) Machine learning Mathematics MATHEMATICS / General Matrix decomposition Memory access pattern Naming convention (programming) Network simulation Numerical analysis Numerical linear algebra Online auction PageRank Parallel computing Parameter (computer programming) Patch (computing) Peer-to-peer Personalization Personalized search Polynomial long division Power iteration Proportionality (mathematics) QR algorithm QR decomposition Randomized algorithm Ranking (information retrieval) Rate of convergence Regularization (mathematics) Replication (computing) Repository (version control) Reputation management Reputation system Robustness (computer science) Sampling (statistics) Scientific notation Search algorithm Selection algorithm Self-organizing systems Self-organizing systems -- Data processing Server (computing) Sign (mathematics) Technology Topic-Sensitive PageRank Tree (data structure) Upload Variable (computer science) Variable (mathematics) Web search engine |
| SubjectTermsDisplay | Database searching |
| TableOfContents | Numerical algorithms for personalized search in self-organizing information networks -- Contents -- Tables -- Figures -- Acknowledgments -- Chapter One: Introduction -- Part I: World Wide Web -- Chapter Two: PageRank -- Chapter Three: The Second Eigenvalue of the Google Matrix -- Chapter Four: The Condition Number of the PageRank Problem -- Chapter Five: Extrapolation Algorithms -- Chapter Six: Adaptive PageRank -- Chapter Seven: BlockRank -- Part II: P2P Networks -- Chapter Eight: Query-Cycle Simulator -- Chapter Nine: EigenTrust -- Chapter Ten: Adaptive P2P Topologies -- Chapter Eleven: Conclusion -- Bibliography Front Matter Table of Contents Tables Figures Acknowledgments Chapter One: Introduction Chapter Two: PageRank Chapter Three: The Second Eigenvalue of the Google Matrix Chapter Four: The Condition Number of the PageRank Problem Chapter Five: Extrapolation Algorithms Chapter Six: Adaptive PageRank Chapter Seven: BlockRank Chapter Eight: Query-Cycle Simulator Chapter Nine: EigenTrust Chapter Ten: Adaptive P2P Topologies Chapter Eleven: Conclusion Bibliography 10.4.1 Malicious Peers Move to Fringe -- 10.4.2 Freeriders Move to Fringe -- 10.4.3 Active Peers Are Rewarded -- 10.4.4 Efficient Topology -- 10.5 Threat Scenarios -- 10.5.1 Threat Model A -- 10.5.2 Threat Model B -- 10.5.3 Threat Model C -- 10.6 Related Work -- 10.7 Discussion -- Chapter 11 Conclusion -- Bibliography Intro -- Numerical Algorithms for Personalized Search in Self-organizing Information Networks -- Contents -- Tables -- Figures -- Acknowledgments -- Chapter 1 Introduction -- 1.1 World Wide Web -- 1.2 P2P Networks -- 1.3 Contributions -- PART I WORLD WIDE WEB -- Chapter 2 PageRank -- 2.1 PageRank Basics -- 2.2 Notation and Mathematical Preliminaries -- 2.3 Power Method -- 2.3.1 Formulation -- 2.3.2 Operation Count -- 2.3.3 Convergence -- 2.4 Experimental Setup -- 2.5 Related Work -- 2.5.1 Fast Eigenvector Computation -- 2.5.2 PageRank -- Chapter 3 The Second Eigenvalue of the Google Matrix -- 3.1 Introduction -- 3.2 Theorems -- 3.3 Proof of Theorem 1 -- 3.4 Proof of Theorem 2 -- 3.5 Implications -- 3.6 Theorems Used -- Chapter 4 The Condition Number of the PageRank Problem -- 4.1 Theorem 6 -- 4.2 Proof of Theorem 6 -- 4.3 Implications -- Chapter 5 Extrapolation Algorithms -- 5.1 Introduction -- 5.2 Aitken Extrapolation -- 5.2.1 Formulation -- 5.2.2 Operation Count -- 5.2.3 Experimental Results -- 5.2.4 Discussion -- 5.3 Quadratic Extrapolation -- 5.3.1 Formulation -- 5.3.2 Operation Count -- 5.3.3 Experimental Results -- 5.3.4 Discussion -- 5.4 Power Extrapolation -- 5.4.1 Simple Power Extrapolation -- 5.4.2 A2 Extrapolation -- 5.4.3 Ad Extrapolation -- 5.5 Measures of Convergence -- Chapter 6 Adaptive PageRank -- 6.1 Introduction -- 6.2 Distribution of Convergence Rates -- 6.3 Adaptive PageRank Algorithm -- 6.3.1 Algorithm Intuition -- 6.3.2 Filter-based Adaptive PageRank -- 6.4 Experimental Results -- 6.5 Extensions -- 6.5.1 Further Reducing Redundant Computation -- 6.5.2 Using the Matrix Ordering from the Previous Computation -- 6.6 Discussion -- Chapter 7 BlockRank -- 7.1 Block Structure of the Web -- 7.1.1 Block Sizes -- 7.1.2 The GeoCities Effect -- 7.2 BlockRank Algorithm -- 7.2.1 Overview of BlockRank Algorithm 7.2.2 Computing Local PageRanks -- 7.2.3 Estimating the Relative Importance of Each Block -- 7.2.4 Approximating Global PageRank Using Local PageRank and BlockRank -- 7.2.5 Using This Estimate as a Start Vector -- 7.3 Advantages of BlockRank -- 7.4 Experimental Results -- 7.5 Discussion -- 7.6 Personalized PageRank -- 7.6.1 Inducing Random Jump Probabilities over Pages -- 7.6.2 Using "Better" Local PageRanks -- 7.6.3 Experiments -- 7.6.4 Topic-Sensitive PageRank -- 7.6.5 Pure BlockRank -- PART II P2P NETWORKS -- Chapter 8 Query-Cycle Simulator -- 8.1 Challenges in Empirical Evaluation of P2P Algorithms -- 8.2 The Query-Cycle Model -- 8.3 Basic Properties -- 8.3.1 Network Topology -- 8.3.2 Joining the Network -- 8.3.3 Query Propagation -- 8.4 Peer-Level Properties -- 8.5 Content Distribution Model -- 8.5.1 Data Volume -- 8.5.2 Content Type -- 8.6 Peer Behavior Model -- 8.6.1 Uptime and Session Duration -- 8.6.2 Query Activity -- 8.6.3 Queries -- 8.6.4 Query Responses -- 8.6.5 Downloads -- 8.7 Network Parameters -- 8.7.1 Topology -- 8.7.2 Bandwidth -- 8.8 Discussion -- Chapter 9 EigenTrust -- 9.1 Design Considerations -- 9.2 Reputation Systems -- 9.3 EigenTrust -- 9.3.1 Normalizing Local Trust Values -- 9.3.2 Aggregating Local Trust Values -- 9.3.3 Probabilistic Interpretation -- 9.3.4 Basic EigenTrust -- 9.3.5 Practical Issues -- 9.3.6 Distributed EigenTrust -- 9.3.7 Algorithm Complexity -- 9.4 Secure EigenTrust -- 9.4.1 Algorithm Description -- 9.4.2 Discussion -- 9.5 Using Global Trust Values -- 9.6 Experiments -- 9.6.1 Load Distribution in a Trust-based Network -- 9.6.2 Threat Models -- 9.7 Related Work -- 9.8 Discussion -- Chapter 10 Adaptive P2P Topologies -- 10.1 Introduction -- 10.2 Interaction Topologies -- 10.3 Adaptive P2P Topologies -- 10.3.1 Local Trust Scores -- 10.3.2 Protocol -- 10.3.3 Practical Issues -- 10.4 Empirical Results Chapter Four. The Condition Number of the PageRank Problem Chapter Eight. Query-Cycle Simulator Chapter Ten. Adaptive P2P Topologies Chapter Two. PageRank Figures - / PART II. P2P Networks -- Chapter Six. Adaptive PageRank Chapter Eleven. Conclusion Chapter Three. The Second Eigenvalue of the Google Matrix Contents Chapter One. Introduction Acknowledgments Chapter Seven. BlockRank Frontmatter -- Chapter Nine. Eigen Trust Tables PART I. World Wide Web -- Chapter Five. Extrapolation Algorithms Bibliography |
| Title | Numerical algorithms for personalized search in self-organizing information networks |
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