false http://photo.goodreads.com/books/1187645251m/1741472.jpg 1741472 1 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.<br /> <br /> <em>Programming Collective Intelligence</em> 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: <ul> <li>Collaborative filtering techniques that enable online retailers to recommend products or media</li> <li>Methods of clustering to detect groups of similar items in a large dataset</li> <li>Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm</li> <li>Optimization algorithms that search millions of possible solutions to a problem and choose the best one</li> <li>Bayesian filtering, used in spam filters for classifying documents based on word types and other features</li> <li>Using decision trees not only to make predictions, but to model the way decisions are made</li> <li>Predicting numerical values rather than classifications to build price models</li> <li>Support vector machines to match people in online dating sites</li> <li>Non-negative matrix factorization to find the independent features in a dataset</li> <li>Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game</li> </ul> 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.<br /> <br /> "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."<br /> -- Dan Russell, Google <br /> <br /> "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."<br /> -- Tim Wolters, CTO, Collective Intellect 1739097 16 8 2007 Programming Collective Intelligence: Building Smart Web 2.0 Applications total:100|5:33|4:41|3:23|2:3|1:0| 100 404 279 25 3 3 false false Mon Oct 01 00:00:00 -0700 2007 Tue Nov 06 19:16:47 -0800 2007 Tue Nov 06 19:32:21 -0800 2007
...more...]]> 5 1 true true Fri Feb 01 00:00:00 -0800 2008 Sun Feb 17 18:00:35 -0800 2008 Thu Feb 28 04:15:24 -0800 2008 If it works, it's not AI.

Segaran's book is getting a lot of buzz right now and for good reason. It's a great survey of some common classification and recognition techniques useful for providing critical services associated with "Web 2.0". The explanations and code are easily...more...]]> 5 0 false false Sun Oct 18 00:00:00 -0700 2009 Tue Oct 06 18:02:03 -0700 2009 Sun Oct 18 04:26:05 -0700 2009 more...]]> 3 0 false false Thu Sep 10 08:43:46 -0700 2009 Mon Sep 14 10:54:13 -0700 2009
- determine people whose taste in movies is most similar to your own
- given a document, guess which category it belongs in
- figure out what bands you might be interes...more...]]> 2 0 false false Thu Aug 13 00:00:00 -0700 2009 Thu Aug 13 23:32:15 -0700 2009 Fri Aug 14 23:36:05 -0700 2009
Making Recommendations:
He basically says to use Pearson correlation on item/user vectors for item-...more...]]> 4 0 false false Wed Nov 28 16:50:58 -0800 2007 Wed Jun 25 10:32:52 -0700 2008
There's a few th...more...]]> 3 0 false false Sat Aug 01 00:00:00 -0700 2009 Tue Mar 11 15:20:27 -0700 2008 Thu Sep 24 21:20:04 -0700 2009 more...]]> 3 0 false false Sat Dec 01 00:00:00 -0800 2007 Tue Jan 01 09:08:01 -0800 2008 Tue Jan 01 09:29:47 -0800 2008
So far I've seen how to calculate movie recommendations from a list of c...more...]]> 3 0 false false Thu Jan 01 00:00:00 -0800 2009 Fri Dec 26 20:28:49 -0800 2008 Thu Jan 01 09:02:46 -0800 2009 more...]]> 4 0 false false Mon Sep 01 00:00:00 -0700 2008 Mon Feb 16 17:31:09 -0800 2009 Mon Feb 16 17:33:05 -0800 2009 0 0 false false Tue Apr 07 01:50:25 -0700 2009 Tue Apr 07 01:50:51 -0700 2009 5 0 false false Fri Jul 25 00:00:00 -0700 2008 Wed Jun 11 00:28:35 -0700 2008 Thu Jul 31 17:58:35 -0700 2008 more...]]> 4 0 false false Tue Feb 10 10:41:15 -0800 2009 Thu Jan 08 17:56:36 -0800 2009 Tue Feb 10 10:41:15 -0800 2009 5 0 false false Sat Dec 01 00:00:00 -0800 2007 Mon Dec 17 19:58:03 -0800 2007 Fri Dec 21 20:33:44 -0800 2007 5 0 false false Fri Oct 17 14:02:40 -0700 2008 Wed Oct 15 15:00:41 -0700 2008 Fri Oct 17 14:02:40 -0700 2008 4 0 false false Sun Jul 13 00:00:00 -0700 2008 Tue Jul 29 19:27:18 -0700 2008 Tue Jul 29 19:29:01 -0700 2008 4 0 false false Wed Nov 14 19:12:21 -0800 2007 Sun Sep 16 18:34:50 -0700 2007 Wed Nov 14 19:12:05 -0800 2007 4 0 false false Wed Aug 27 14:04:01 -0700 2008 Wed Aug 27 14:06:17 -0700 2008 0 0 false false Thu Jan 29 05:08:11 -0800 2009 Sun Apr 12 19:19:24 -0700 2009 4 0 false false Thu Sep 11 15:17:24 -0700 2008 Thu Sep 11 15:18:01 -0700 2008 http://www.goodreads.com/book_link/follow/8?book_id=1741472