HIGHLIGHT Learn the techniques used by Google, Netflix, and Amazon to transform raw data into actionable information―including recommendations, predictions, and intelligent search. DESCRIPTION Web 2.0 applications provide a rich user experience, but the parts you can't see are just as important―and impressive. They use powerful techniques to process information intelligently and offer features based on patterns and relationships in data. Algorithms of the Intelligent Web shows readers how to use the same techniques employed by household names like Google Ad Sense, Netflix, and Amazon to transform 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. Readers learn to build Netflix-style recommendation engines, and how to apply the same techniques to social-networking sites. See how click-trace analysis can result in smarter ad rotations. All the examples are designed both to be reused and to illustrate a general technique― an algorithm―that applies to a broad range of scenarios. As they work through the book’s many examples, readers learn about recommendation systems, search and ranking, automatic grouping of similar objects, classification of objects, forecasting models, and autonomous agents. They 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. KEY POINTS • Create recommendations like those on Netflix and Amazon • Implement Google’s Pagerank and the HITS algorithm • Discover matches on social-networking sites • Business techniques like sorting email based on content, targeted advertising, and fraud detection MARKET INFORMATION The fields of Collective Intelligence and Web 2.0 are driving much of the interest in new web development techniques. This book is front-and-center in this hot area.

�ber die Autorin bzw. den Autor

Dr. Haralambos Marmanis 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. MANNING

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