Special Features: Learning Elements:· 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 About The Book: 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.

This reference presents both fundamental and advanced topics and related to Web operations. Using an integrated approach, the authors describe the basics as well as latest trends in the area. They cover agent-based Web, wrapper induction, Web mining, information retrieval, Web knowledge management, and social networks. The text includes a host of examples and over 100 illustrations that clarify complex material. It also contains many bibliographical notes, end-of-chapter exercises, glossaries, and practice questions with solutions/hints.

This book constitutes the refereed proceedings of the First Asia-Pacific Conference on Web Intelligence, WI 2001, held in Maebashi City, Japan, in October 2001. The 28 revised full papers and 45 revised short papers presented were carefully reviewed and selected from 153 full-length paper submissions. Also included are an introductory survey and six invited presentations. The book offers topical sections on Web information systems environments and foundations, Web human-media engineering, Web information management, Web information retrieval, Web agents, Web mining and farming, and Web-based applications.

As we use the Web for social networking, shopping, and news, we leave a personal trail. These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion. These are just basic examples of the growth of "Web intelligence", as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected. Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.

The World Wide Web has become an extremely popular way of publishing and distributing electronic resources. Though the Web is rich with information, collecting and making sense of this data is difficult because it is rather unorganized. Building an Intelligent Web introduces students and professionals to the state-of-the art development of Web Intelligence techniques and teaches how to apply these techniques to develop the next generation of intelligent Web sites. Each chapter contains theoretical bases, which are also illustrated with the help of simple numeric examples, followed by practical implementation. Students will find Building an Intelligent Web to be an active and exciting introduction to advanced Web mining topics. Topics covered include Web Intelligence, Information Retrieval, Semantic Web, Classification and Association Rules, SQL, Database Theory, Applications to e-commerce and Bioinformatics, Clustering, Modeling Web Topology, and much more!

Web technologies have become a vital element within educational, professional, and social settings as they have the potential to improve performance and productivity across organizations. Artificial Intelligence Technologies and the Evolution of Web 3.0 brings together emergent research and best practices surrounding the effective usage of Web 3.0 technologies in a variety of environments. Featuring the latest technologies and applications across industries, this publication is a vital reference source for academics, researchers, students, and professionals who are interested in new ways to use intelligent web technologies within various settings.

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect