Web 2.0 applications are best known for providing a rich user experience, but the parts you can't see are just as important—and impressive. Many Web 2.0 applications use powerful techniques to process information intelligently and offer features based on patterns and relationships in the data that couldn't be discovered manually. Successful examples of these Algorithms of the Intelligent Web include household names like Google Ad Sense, Netflix, and Amazon. These applications use the internet as a platform that not only gathers data at an ever-increasing pace but also systematically transforms the raw data into actionable information.

Algorithms of the Intelligent Web is an example-driven blueprint for creating applications that collect, analyze, and act on the massive quantities of data users leave in their wake as they use the web. You'll learn how to build Amazon- and Netflix-style recommendation engines, and how the same techniques apply to people matches on social-networking sites. See how click-trace analysis can result in smarter ad rotations. With a plethora of examples and extensive detail, this book shows you how to build Web 2.0 applications that are as smart as your users.

As you work through the book's many examples, you'll learn about recommendation systems, search and ranking, automatic grouping of similar objects, classification of objects, forecasting models, and autonomous agents. You'll also become familiar with a large number of open-source libraries and SDKs, and freely available APIs from the hottest sites on the internet, such as Facebook, Google, eBay, and Yahoo.

• How to create recommendations just like those on Netflix and Amazon
• How to implement Google's Pagerank algorithm
• How to discover matches on social-networking sites
• How to organize the discussions on your favorite news group
• How to select topics of interest from shared bookmarks
• How to leverage user clicks
• How to categorize emails based on their content
• How to build applications that do targeted advertising
• How to implement fraud detection

To get the most from this book, you should have a good foundation in Java programming and a general understanding of internet technology.

Dr. Haralambos (Babis) Marmanis is a pioneer in the adoption of machine learning techniques for industrial solutions, and also a world expert in supply management. He has about twenty years of experience in developing professional software. Currently, he is the director of R&D and chief architect, for expense management solutions, at Emptoris, Inc. Babis holds a Ph.D. in applied mathematics from Brown University, an M.S. degree in theoretical and applied mechanics from the University of Illinois at Urbana-Champaign, and B.S. and M.S. degrees in civil engineering from the Aristotle University of Thessaloniki in Greece. He was the recipient of the Sigma Xi award for innovative research in 2000, and he is the author of numerous publications in peer-reviewed international scientific journals, conferences, and technical periodicals.

Dmitry Babenko is the lead for the data warehouse infrastructure at Emptoris, Inc. He is a software engineer and architect with 13 years of experience in the IT industry. He has designed and built a wide variety of applications and infrastructure frameworks for banking, insurance, supply-chain management, and business intelligence companies. He received a M.S. degree in computer science from Belarussian State University of Informatics and Radioelectronics.