contents


preface
acknowledgments
about this book
 
1 What is the intelligent web?
1.1 Examples of intelligent web applications
1.2 Basic elements of intelligent applications
1.3 What applications can benefit from intelligence?
1.4 How can I build intelligence in my own application?
1.5 Machine learning, data mining, and all that
1.6 Eight fallacies of intelligent applications
1.7 Summary
1.8 References
2 Searching
2.1 Searching with Lucene
2.2 Why search beyond indexing?
2.3 Improving search results based on link analysis
2.4 Improving search results based on user clicks
2.5 Ranking Word, PDF, and other documents without links
2.6 Large-scale implementation issues
2.7 Is what you got what you want? Precision and recall
2.8 Summary
2.9 To do
2.10 References
3 Creating suggestions and recommendations
3.1 An online music store: the basic concepts
3.2 How do recommendation engines work?
3.3 Recommending friends, articles, and news stories
3.4 Recommending movies on a site such as Netflix.com
3.5 Large-scale implementation and evaluation issues
3.6 Summary
3.7 To Do
3.8 References
4 Clustering: grouping things together
4.1 The need for clustering
4.2 An overview of clustering algorithms
4.3 Link-based algorithms
4.4 The k-means algorithm
4.5 Robust Clustering Using Links (ROCK)
4.6 DBSCAN
4.7 Clustering issues in very large datasets
4.8 Summary
4.9 To Do
4.10 References
5 Classification: placing things where they belong
5.1 The need for classification
5.2 An overview of classifiers
5.3 Automatic categorization of emails and spam filtering
5.4 Fraud detection with neural networks
5.5 Are your results credible?
5.6 Classification with very large datasets
5.7 Summary
5.8 To do
5.9 References
6 Combining classifiers
6.1 Credit worthiness: a case study for combining classifiers
6.2 Credit evaluation with a single classifier
6.3 Comparing multiple classifiers on the same data
6.4 Bagging: bootstrap aggregating
6.5 Boosting: an iterative improvement approach
6.6 Summary
6.7 To Do
6.8 References
7 Putting it all together: an intelligent news portal
7.1 An overview of the functionality
7.2 Getting and cleansing content
7.3 Searching for news stories
7.4 Assigning news categories
7.5 Building news groups with the NewsProcessor class
7.6 Dynamic content based on the user’s ratings
7.7 Summary
7.8 To do
7.9 References

 
appendix A Introduction to BeanShell
appendix B Web crawling
appendix C Mathematical refresher
appendix D Natural language processing
appendix E Neural networks
index