contents


foreword
preface
acknowledgments
about this book

Part 1 Gathering data for intelligence

1 Understanding collective intelligence
1.1 What is collective intelligence?
1.2 CI in web applications
1.3 Classifying intelligence
1.4 Summary
1.5 Resources
2 Learning from user interactions
2.1 Architecture for applying intelligence
2.2 Basics of algorithms for applying CI
2.3 Forms of user interaction
2.4 Converting user interaction into collective intelligence
2.5 Summary
2.6 Resources
3 Extracting intelligence from tags
3.1 Introduction to tagging
3.2 How to leverage tags
3.3 Extracting intelligence from user tagging: an example
3.4 Scalable persistence architecture for tagging
3.5 Building tag clouds
3.6 Finding similar tags
3.7 Summary
3.8 Resources
4 Extracting intelligence from content
4.1 Content types and integration
4.2 The main CI-related content types
4.3 Extracting intelligence step by step
4.4 Simple and composite content types
4.5 Summary
4.6 Resources
5 Searching the blogosphere
5.1 Introducing the blogosphere
5.2 Building a framework to search the blogosphere
5.3 Implementing the base classes
5.4 Integrating Technorati
5.5 Integrating Bloglines
5.6 Integrating providers using RSS
5.7 Summary
5.8 Resources
6 Intelligent web crawling
6.1 Introducing web crawling
6.2 Building an intelligent crawler step by step
6.3 Scalable crawling with Nutch
6.4 Summary
6.5 Resources

Part 2 Deriving intelligence

7 Data mining: process, toolkits, and standards
7.1 Core concepts of data mining
7.2 Using an open source data mining framework: WEKA
7.3 Standard data mining API: Java Data Mining (JDM)
7.4 Summary
7.5 Resources
8 Building a text analysis toolkit
8.1 Building the text analyzers
8.2 Building the text analysis infrastructure
8.3 Use cases for applying the framework
8.4 Summary
8.5 Resources
9 Discovering patterns with clustering
9.1 Clustering blog entries
9.2 Leveraging WEKA for clustering
9.3 Clustering using the JDM APIs
9.4 Summary
9.5 Resources
10 Making predictions
10.1 Classification fundamentals
10.2 Classifying blog entries using WEKA APIs
10.3 Regression fundamentals
10.4 Regression using WEKA
10.5 Classification and regression using JDM
10.6 Summary
10.7 Resources

Part 3 Applying intelligence in your application

11 Intelligent search
11.1 Search fundamentals
11.2 Indexing with Lucene
11.3 Searching with Lucene
11.4 Useful tools and frameworks
11.5 Approaches to intelligent search
11.6 Summary
11.7 Resources 347
12 Building a recommendation engine
12.1 Recommendation engine fundamentals
12.2 Content-based analysis
12.3 Collaborative filtering
12.4 Real-world solutions
12.5 Summary
12.6 Resources

 
index