Algorithms of the Intelligent Web

Algorithms of the Intelligent Web

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Learn the techniques used by Google, Netflix, and Amazon to transform raw

data into actionable information including recommendations, predictions,

and intelligent search.


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.


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


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.
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Product details

  • Paperback | 325 pages
  • 185.42 x 231.14 x 20.32mm | 612.35g
  • New York, United States
  • English
  • 1933988665
  • 9781933988665
  • 435,927

About Haralambos Marmanis

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.

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Rating details

120 ratings
3.6 out of 5 stars
5 15% (18)
4 41% (49)
3 36% (43)
2 7% (8)
1 2% (2)
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