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Abstract

The second edition of Understanding Search Engines: Mathematical Modeling and Text Retrieval follows the basic premise of the first edition by discussing many of the key design issues for building search engines and emphasizing the important role that applied mathematics can play in improving information retrieval. The authors discuss important data structures, algorithms, and software as well as user-centered issues such as interfaces, manual indexing, and document preparation. Significant changes bring the text up to date on current information retrieval methods: for example the addition of a new chapter on link-structure algorithms used in search engines such as Google. The chapter on user interface has been rewritten to specifically focus on search engine usability. In addition the authors have added new recommendations for further reading and expanded the bibliography, and have updated and streamlined the index to make it more reader friendly.

Content

Inhalt: Introduction Document File Preparation - Manual Indexing - Information Extraction - Vector Space Modeling - Matrix Decompositions - Query Representations - Ranking and Relevance Feedback - Searching by Link Structure - User Interface - Book Format Document File Preparation Document Purification and Analysis - Text Formatting - Validation - Manual Indexing - Automatic Indexing - Item Normalization - Inverted File Structures - Document File - Dictionary List - Inversion List - Other File Structures Vector Space Models Construction - Term-by-Document Matrices - Simple Query Matching - Design Issues - Term Weighting - Sparse Matrix Storage - Low-Rank Approximations Matrix Decompositions QR Factorization - Singular Value Decomposition - Low-Rank Approximations - Query Matching - Software - Semidiscrete Decomposition - Updating Techniques Query Management Query Binding - Types of Queries - Boolean Queries - Natural Language Queries - Thesaurus Queries - Fuzzy Queries - Term Searches - Probabilistic Queries Ranking and Relevance Feedback Performance Evaluation - Precision - Recall - Average Precision - Genetic Algorithms - Relevance Feedback Searching by Link Structure HITS Method - HITS Implementation - HITS Summary - PageRank Method - PageRank Adjustments - PageRank Implementation - PageRank Summary User Interface Considerations General Guidelines - Search Engine Interfaces - Form Fill-in - Display Considerations - Progress Indication - No Penalties for Error - Results - Test and Retest - Final Considerations Further Reading

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Content

Inhalt: Chapter 1. Introduction to Web Search Engines: 1.1 A Short History of Information Retrieval - 1.2 An Overview of Traditional Information Retrieval - 1.3 Web Information Retrieval Chapter 2. Crawling, Indexing, and Query Processing: 2.1 Crawling - 2.2 The Content Index - 2.3 Query Processing Chapter 3. Ranking Webpages by Popularity: 3.1 The Scene in 1998 - 3.2 Two Theses - 3.3 Query-Independence Chapter 4. The Mathematics of Google's PageRank: 4.1 The Original Summation Formula for PageRank - 4.2 Matrix Representation of the Summation Equations - 4.3 Problems with the Iterative Process - 4.4 A Little Markov Chain Theory - 4.5 Early Adjustments to the Basic Model - 4.6 Computation of the PageRank Vector - 4.7 Theorem and Proof for Spectrum of the Google Matrix Chapter 5. Parameters in the PageRank Model: 5.1 The a Factor - 5.2 The Hyperlink Matrix H - 5.3 The Teleportation Matrix E Chapter 6. The Sensitivity of PageRank; 6.1 Sensitivity with respect to alpha - 6.2 Sensitivity with respect to H - 6.3 Sensitivity with respect to vT - 6.4 Other Analyses of Sensitivity - 6.5 Sensitivity Theorems and Proofs Chapter 7. The PageRank Problem as a Linear System: 7.1 Properties of (I - alphaS) - 7.2 Properties of (I - alphaH) - 7.3 Proof of the PageRank Sparse Linear System Chapter 8. Issues in Large-Scale Implementation of PageRank: 8.1 Storage Issues - 8.2 Convergence Criterion - 8.3 Accuracy - 8.4 Dangling Nodes - 8.5 Back Button Modeling