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E-grāmata: Numerical Algorithms for Personalized Search in Self-organizing Information Networks

  • Formāts: 160 pages
  • Izdošanas datums: 07-Sep-2010
  • Izdevniecība: Princeton University Press
  • Valoda: eng
  • ISBN-13: 9781400837069
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Numerical Algorithms for Personalized Search in Self-organizing Information NetworksPalielināt
  • Formāts: 160 pages
  • Izdošanas datums: 07-Sep-2010
  • Izdevniecība: Princeton University Press
  • Valoda: eng
  • ISBN-13: 9781400837069
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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.

Recenzijas

"The writing style is extremely clear, and the book is accessible to readers both within and outside of the field."Chen Greif, University of British Columbia "The clarity of presentation makes this book accessible to a broad audience. The scholarship is thorough and sound, and the experimental results are presented in a precise and detailed fashion."Taher Haveliwala, QForge Labs "Kamvar helped establish a foundation for P2P search and this book provides an authoritative record and source for his excellent work in this area."Andrew Tomkins, Google

Papildus informācija

The writing style is extremely clear, and the book is accessible to readers both within and outside of the field. -- Chen Greif, University of British Columbia The clarity of presentation makes this book accessible to a broad audience. The scholarship is thorough and sound, and the experimental results are presented in a precise and detailed fashion. -- Taher Haveliwala, QForge Labs Kamvar helped establish a foundation for P2P search and this book provides an authoritative record and source for his excellent work in this area. -- Andrew Tomkins, Google
Tables
ix
Figures
xi
Acknowledgments xv
Chapter 1 Introduction
1(4)
1.1 World Wide Web
1(1)
1.2 P2P Networks
2(1)
1.3 Contributions
2(3)
PART I World Wide Web
5(68)
Chapter 2 PageRank
7(8)
2.1 PageRank Basics
7(2)
2.2 Notation and Mathematical Preliminaries
9(1)
2.3 Power Method
10(3)
2.3.1 Formulation
10(2)
2.3.2 Operation Count
12(1)
2.3.3 Convergence
12(1)
2.4 Experimental Setup
13(1)
2.5 Related Work
13(2)
2.5.1 Fast Eigenvector Computation
13(1)
2.5.2 PageRank
14(1)
Chapter 3 The Second Eigenvalue of the Google Matrix
15(5)
3.1 Introduction
15(1)
3.2 Theorems
15(1)
3.3 Proof of Theorem 1
15(2)
3.4 Proof of Theorem 2
17(1)
3.5 Implications
18(1)
3.6 Theorems Used
19(1)
Chapter 4 The Condition Number of the PageRank Problem
20(3)
4.1 Theorem 6
20(1)
4.2 Proof of Theorem 6
20(1)
4.3 Implications
21(2)
Chapter 5 Extrapolation Algorithms
23(19)
5.1 Introduction
23(1)
5.2 Aitken Extrapolation
23(4)
5.2.1 Formulation
23(2)
5.2.2 Operation Count
25(1)
5.2.3 Experimental Results
26(1)
5.2.4 Discussion
26(1)
5.3 Quadratic Extrapolation
27(8)
5.3.1 Formulation
27(3)
5.3.2 Operation Count
30(1)
5.3.3 Experimental Results
30(4)
5.3.4 Discussion
34(1)
5.4 Power Extrapolation
35(5)
5.4.1 Simple Power Extrapolation
35(1)
5.4.2 A2 Extrapolation
35(2)
5.4.3 Ad Extrapolation
37(3)
5.5 Measures of Convergence
40(2)
Chapter 6 Adaptive PageRank
42(9)
6.1 Introduction
42(1)
6.2 Distribution of Convergence Rates
42(2)
6.3 Adaptive PageRank Algorithm
44(4)
6.3.1 Algorithm Intuition
45(1)
6.3.2 Filter-based Adaptive PageRank
46(2)
6.4 Experimental Results
48(1)
6.5 Extensions
48(2)
6.5.1 Further Reducing Redundant Computation
48(2)
6.5.2 Using the Matrix Ordering from the Previous Computation
50(1)
6.6 Discussion
50(1)
Chapter 7 BlockRank
51(22)
7.1 Block Structure of the Web
51(4)
7.1.1 Block Sizes
54(1)
7.1.2 The GeoCities Effect
55(1)
7.2 BlockRank Algorithm
55(8)
7.2.1 Overview of BlockRank Algorithm
56(1)
7.2.2 Computing Local PageRanks
57(3)
7.2.3 Estimating the Relative Importance of Each Block
60(1)
7.2.4 Approximating Global PageRank Using Local PageRank and BlockRank
61(1)
7.2.5 Using This Estimate as a Start Vector
62(1)
7.3 Advantages of BlockRank
63(1)
7.4 Experimental Results
64(3)
7.5 Discussion
67(1)
7.6 Personalized PageRank
67(6)
7.6.1 Inducing Random Jump Probabilities over Pages
68(1)
7.6.2 Using "Better" Local PageRanks
68(1)
7.6.3 Experiments
69(1)
7.6.4 Topic-Sensitive PageRank
70(1)
7.6.5 Pure BlockRank
71(2)
PART II P2P Networks
73(62)
Chapter 8 Query-Cycle Simulator
75(9)
8.1 Challenges in Empirical Evaluation of P2P Algorithms
75(1)
8.2 The Query-Cycle Model
75(1)
8.3 Basic Properties
76(1)
8.3.1 Network Topology
76(1)
8.3.2 Joining the Network
76(1)
8.3.3 Query Propagation
76(1)
8.4 Peer-Level Properties
77(1)
8.5 Content Distribution Model
78(2)
8.5.1 Data Volume
78(1)
8.5.2 Content Type
78(2)
8.6 Peer Behavior Model
80(2)
8.6.1 Uptime and Session Duration
80(1)
8.6.2 Query Activity
81(1)
8.6.3 Queries
81(1)
8.6.4 Query Responses
81(1)
8.6.5 Downloads
82(1)
8.7 Network Parameters
82(1)
8.7.1 Topology
82(1)
8.7.2 Bandwidth
82(1)
8.8 Discussion
83(1)
Chapter 9 Eigen Trust
84(24)
9.1 Design Considerations
84(1)
9.2 Reputation Systems
85(1)
9.3 EigenTrust
86(4)
9.3.1 Normalizing Local Trust Values
86(1)
9.3.2 Aggregating Local Trust Values
87(1)
9.3.3 Probabilistic Interpretation
87(1)
9.3.4 Basic EigenTrust
87(1)
9.3.5 Practical Issues
88(1)
9.3.6 Distributed EigenTrust
89(1)
9.7 Algorithm Complexity
90(1)
9.4 Secure EigenTrust
91(3)
9.4.1 Algorithm Description
92(1)
9.4.2 Discussion
93(1)
9.5 Using Global Trust Values
94(1)
9.6 Experiments
95(11)
9.6.1 Load Distribution in a Trust-based Network
95(3)
9.6.2 Threat Models
98(8)
9.7 Related Work
106(1)
9.8 Discussion
106(2)
Chapter 10 Adaptive P2P Topologies
108(25)
10.1 Introduction
108(1)
10.2 Interaction Topologies
109(1)
10.3 Adaptive P2P Topologies
109(6)
10.3.1 Local Trust Scores
109(1)
10.3.2 Protocol
110(2)
10.3.3 Practical Issues
112(3)
10.4 Empirical Results
115(11)
10.4.1 Malicious Peers Move to Fringe
115(3)
10.4.2 Freeriders Move to Fringe
118(1)
10.4.3 Active Peers Are Rewarded
119(1)
10.4.4 Efficient Topology
120(6)
10.5 Threat Scenarios
126(5)
10.5.1 Threat Model A
126(2)
10.5.2 Threat Model B
128(2)
10.5.3 Threat Model C
130(1)
10.6 Related Work
131(1)
10.7 Discussion
132(1)
Chapter 11 Conclusion
133(2)
Bibliography 135
Sep Kamvar is a consulting assistant professor of computational mathematics at Stanford University. From 2003 to 2007, he was the engineering lead for personalization at Google. He is the founder and former CEO of Kaltix, a personalized search engine acquired by Google in 2003.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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