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Modeling the Internet and the Web: Probabilistic Methods and Algorithms
ISBN: 978-0-470-84906-4
Hardcover
306 pages
July 2003
US $110.00 Add to Cart

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Preface.

1 Mathematical Background.

1.1 Probability and Learning from a Bayesian Perspective.

1.2 Parameter Estimation from Data.

1.3 Mixture Models and the Expectation Maximization Algorithm.

1.4 Graphical Models.

1.5 Classification.

1.6 Clustering.

1.7 Power-Law Distributions.

1.8 Exercises.

2 Basic WWW Technologies.

2.1 Web Documents.

2.2 Resource Identifiers: URI, URL, and URN.

2.3 Protocols.

2.4 Log Files.

2.5 Search Engines.

2.6 Exercises.

3 Web Graphs.

3.1 Internet and Web Graphs.

3.2 Generative Models for the Web Graph and Other Networks.

3.3 Applications.

3.4 Notes and Additional Technical References.

3.5 Exercises.

4 Text Analysis.

4.1 Indexing.

4.2 Lexical Processing.

4.3 Content-Based Ranking.

4.4 Probabilistic Retrieval.

4.5 Latent Semantic Analysis.

4.6 Text Categorization.

4.7 Exploiting Hyperlinks.

4.8 Document Clustering.

4.9 Information Extraction.

4.10 Exercises.

5 Link Analysis.

5.1 Early Approaches to Link Analysis.

5.2 Nonnegative Matrices and Dominant Eigenvectors.

5.3 Hubs and Authorities: HITS.

5.4 PageRank.

5.5 Stability.

5.6 Probabilistic Link Analysis.

5.7 Limitations of Link Analysis.

6 Advanced Crawling Techniques.

6.1 Selective Crawling.

6.2 Focused Crawling.

6.3 Distributed Crawling.

6.4 Web Dynamics.

7 Modeling and Understanding Human Behavior on the Web.

7.1 Introduction.

7.2 Web Data and Measurement Issues.

7.3 Empirical Client-Side Studies of Browsing Behavior.

7.4 Probabilistic Models of Browsing Behavior.

7.5 Modeling and Understanding Search Engine Querying.

7.6 Exercises.

8 Commerce on the Web: Models and Applications.

8.1 Introduction.

8.2 Customer Data on theWeb.

8.3 Automated Recommender Systems.

8.4 Networks and Recommendations.

8.5 Web Path Analysis for Purchase Prediction.

8.6 Exercises.

Appendix A: Mathematical Complements.

A.1 Graph Theory.

A.2 Distributions.

A.3 Singular Value Decomposition.

A.4 Markov Chains.

A.5 Information Theory.

Appendix B: List of Main Symbols and Abbreviations.

References.

Index.

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