1. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.1 Chapter 3 : The Problem of Web Navigation User’s often get “lost in hyperspace” when –Following links on web pages, or –Jumping to and from search engine results. Machine learning can provide a sound basis for improving web intreraction.

2. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.2 Getting lost in hyperspace Figure 3.1: The navigation problem

3. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.3 Getting lost in hyperspace Figure 3.2: Being lost in hyperspace

4. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.4 The Naïve Bayes Classifier: Automatic classification of web pages can widen the scope and size of web directories

5. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.5 Trails should be First-Class Objects Figure 3.3: Example web site

6. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.6 Trails should be First-Class Objects Figure 3.4: Four trails within a web site

7. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.7 Trails should be First-Class Objects Figure 3.5: Query results for “mark research”

8. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.8 Trails should be First-Class Objects Figure 3.6: Relevant trail for “mark research”

9. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.9 Markov chains Markov chains have been extensively studied by statisticians and have been applied in a wide variety of areas.

10. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.10 The probabilities of following links Figure 3.7: Markov chain for example web site

11. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.11 The probabilities of following links Figure 3.8: Two trails in the Markov chain

12. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.12 The probabilities of following links Figure 3.9: Probabilities of the four trails

13. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.13 The relevance of links Figure 3.10: Scoring web pages

14. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.14 The relevance of links Figure 3.11: Constructing a chain from scores

15. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.15 Conflict Between Web Site Owner and Visitor The web site owner has objectives related to the business model of the site, e.g. selling products in an e-commerce site. The objectives of visitors are related to their information needs, e.g. gathering information in an e-commerce site. Web site owners would like to identify their visitors (e.g. via cookies), while visitors may prefer to remain anonymous.

16. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 3.16 Conflict Between Semantics of Web Site and Business Model E.g. the objective of an e-commerce site is to convert visitors into customers. But to keep visitors satisfied a web site must provide solutions to users’ information needs. There must be a balance between web site navigability and the business objectives of the site.