eBook

Numerical algorithms for personalized search in self-organizing information networks

Kamvar, Sep

2010

Overview

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.

• Publisher: Princeton University Press
• Subject: 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
• ISBN: 9780691145037, 0691145032, 1400837065, 9781400837069