ISBN:
9781139572804
Language:
English
Pages:
1 online resource (507 pages)
Parallel Title:
Erscheint auch als
DDC:
621.382
Keywords:
Computer networks..
;
Information networks
;
Electronic books
;
Electronic books
Abstract:
How does the Internet really work? This book explains the technology behind it all, in simple question and answer format.
Abstract:
Cover -- Networked Life -- Title -- Copyright -- Dedication -- Contents -- Preface -- Acknowledgements -- Roadmap -- 1 What makes CDMA work for my smartphone? -- 1.1 A Short Answer -- 1.2 A Long Answer -- 1.2.1 Distributed power control -- 1.2.2 DPC as an optimization solution -- 1.2.3 DPC as a game -- 1.3 Examples -- 1.4 Advanced Materia -- 1.4.1 Iterative power method -- 1.4.2 Outer loop power control -- Summary -- Further Reading -- Problems -- 2 How does Google sell ad spaces? -- 2.1 A Short Answer -- 2.2 A Long Answer -- 2.2.1 When do we need auctions? -- 2.2.2 Auctions as games -- 2.2.3 Single-item auction: Second price -- 2.2.4 Multiple-item auction: Generalized second price (GSP) -- 2.3 Examples -- 2.3.1 Single-item auction on eBay -- 2.3.2 Multiple-item GSP auction in Google -- 2.3.3 Another example of GSP -- 2.4 Advanced Material -- 2.4.1 VCG auction -- 2.4.2 An example -- 2.4.3 Truthful bidding -- 2.4.4 Other considerations -- Summary -- Further Reading -- Problems -- 3 How does Google rank webpages? -- 3.1 A Short Answer -- 3.2 A Long Answer -- 3.2.1 Constructing H -- 3.2.2 Constructing ^H -- 3.2.3 Constructing G -- 3.3 Examples -- 3.4 Advanced Material -- 3.4.1 Generalized PageRank and some basic properties -- 3.4.2 PageRank as the solution to a linear equation -- 3.4.3 Scaling up and speeding up -- 3.4.4 Beyond the basic search -- Summary -- Further Reading -- Problems -- 4 How does Netflix recommend movies? -- 4.1 A Short Answer -- 4.1.1 Recommendation problem -- 4.1.2 The Netix Prize -- 4.1.3 Key ideas -- 4.2 A Long Answer -- 4.2.1 Baseline predictor through least squares -- 4.2.2 Quick detour: Convex optimization -- 4.2.3 Quick detour: Baseline predictor with temporal models -- 4.2.4 Neighborhood method: Similarity measure and weighted prediction -- 4.2.5 Summary -- 4.3 Examples -- 4.3.1 Baseline predictor.
Description / Table of Contents:
Cover; Networked Life; Title; Copyright; Dedication; Contents; Preface; Acknowledgements; Roadmap; 1 What makes CDMA work for my smartphone?; 1.1 A Short Answer; 1.2 A Long Answer; 1.2.1 Distributed power control; 1.2.2 DPC as an optimization solution; 1.2.3 DPC as a game; 1.3 Examples; 1.4 Advanced Materia; 1.4.1 Iterative power method; 1.4.2 Outer loop power control; Summary; Further Reading; Problems; 2 How does Google sell ad spaces?; 2.1 A Short Answer; 2.2 A Long Answer; 2.2.1 When do we need auctions?; 2.2.2 Auctions as games; 2.2.3 Single-item auction: Second price
Description / Table of Contents:
2.2.4 Multiple-item auction: Generalized second price (GSP)2.3 Examples; 2.3.1 Single-item auction on eBay; 2.3.2 Multiple-item GSP auction in Google; 2.3.3 Another example of GSP; 2.4 Advanced Material; 2.4.1 VCG auction; 2.4.2 An example; 2.4.3 Truthful bidding; 2.4.4 Other considerations; Summary; Further Reading; Problems; 3 How does Google rank webpages?; 3.1 A Short Answer; 3.2 A Long Answer; 3.2.1 Constructing H; 3.2.2 Constructing ^H; 3.2.3 Constructing G; 3.3 Examples; 3.4 Advanced Material; 3.4.1 Generalized PageRank and some basic properties
Description / Table of Contents:
3.4.2 PageRank as the solution to a linear equation3.4.3 Scaling up and speeding up; 3.4.4 Beyond the basic search; Summary; Further Reading; Problems; 4 How does Netflix recommend movies?; 4.1 A Short Answer; 4.1.1 Recommendation problem; 4.1.2 The Netix Prize; 4.1.3 Key ideas; 4.2 A Long Answer; 4.2.1 Baseline predictor through least squares; 4.2.2 Quick detour: Convex optimization; 4.2.3 Quick detour: Baseline predictor with temporal models; 4.2.4 Neighborhood method: Similarity measure and weighted prediction; 4.2.5 Summary; 4.3 Examples; 4.3.1 Baseline predictor; 4.3.2 Neighborhood model
Description / Table of Contents:
4.4 Advanced Material4.4.1 Regularization: Robust learning without overfitting; 4.4.2 Latent-factor method: matrix factorization and alternating projection; Summary; Further Reading; Problems; 5 When can I trust an average rating on Amazon?; 5.1 A Short Answer; 5.1.1 Challenges of rating aggregation; 5.1.2 Beyond basic aggregation of ratings; 5.2 A Long Answe; 5.2.1 Averaging a crowd; 5.2.2 Bayesian estimation; 5.2.3 Bayesian ranking; 5.3 Examples; 5.3.1 Bayesian ranking changes order; 5.3.2 Bayesian ranking quantities subjectivity; 5.3.3 What does Amazon do?; 5.4 Advanced Material
Description / Table of Contents:
5.4.1 Averaging sequentially-trained estimatorsSummary; Further Reading; Problems; 6 Why does Wikipedia even work?; 6.1 A Short Answer; 6.2 A Long Answer; 6.2.1 Major types of voting systems; 6.2.2 A counter-intuitive example; 6.2.3 Arrow's impossibility result; 6.2.4 Possibility results; 6.3 Examples; 6.3.1 Sen's impossibility result; 6.3.2 Constructing any counter-example you want; 6.3.3 Connection to prisoner's dilemma; 6.4 Advanced Material; 6.4.1 Bargaining: Interactive offers; 6.4.2 Bargaining: Nash bargaining solution; Summary; Further Reading; Problems
Description / Table of Contents:
7 How do I viralize a YouTube video and tip a Groupon deal?
Note:
Description based on publisher supplied metadata and other sources
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