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Collective Intelligence
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Intelligent Middleware in the Realtime Enterprisehttp://www.dachisgroup.com/2010/04/intelligent-middleware-in-the-realtime-enterprise/ Enterprise microblogging has been gaining in popularity in the last year. Companies like Socialtext, Socialcast, Status.net and others have developed some absolutely stunning and low cost enterprise platforms. Each of these platforms has a rich set of APIs and can be customized with varying degrees of flexibility. Some take their cues from existing enterprise software best practices while others are practically indistinguishable from consumer software. Many smaller organizations simply use consumer tools, such as Skype%u2019s chat rooms, as their microblogging platform.- Web 2.0 - Collective Intelligence - |
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Handbook of Collective Intelligencehttp://scripts.mit.edu/~cci/HCI/index.php?title=Main_Page This Handbook provides a survey of the field of collective intelligence, summarizing what is known, providing references to sources for further information, and suggesting possibilities for future research.- Knowledge Management - Collective Intelligence - |
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Toby Segaran (2007)
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 adataset 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.
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