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 w...
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
Toby Segaran works as a Data Magnate at Metaweb Technologies. Prior to working at Metaweb, he started a biotech software company called Incellico which was later acquired by Genstruct. His book, "Programming Collective Intelligence" has been the best-selling AI book on Amazon for several months. He is the recipient of a National Interest Waiver for "People of Exceptional Abilit...
Toby Segaran works as a Data Magnate at Metaweb Technologies. Prior to working at Metaweb, he started a biotech software company called Incellico which was later acquired by Genstruct. His book, "Programming Collective Intelligence" has been the best-selling AI book on Amazon for several months. He is the recipient of a National Interest Waiver for "People of Exceptional Ability", and currently lives in San Francisco. His blog and other information are located at kiwitobes.com.
Next, get a list of random people to make up the dataset. Fortunately, Hot or Not provides an API call that returns a list of people with specified criteria. In this exam-
ple, the only criteria will be that the people have “meet me” profiles, since only from
these profiles can you get other information like location and interests. Add this
function to hotornot.py: (查看原文)
What Does This Have to Do with the Articles Matrix?
So far, what you have is a matrix of articles with word counts. The goal is to factorize
this matrix, which means finding two smaller matrices that can be multiplied
together to reconstruct this one. The two smaller matrices are:
The features matrix
This matrix has a row for each feature and a column for each word. The values
indicate how important a word is to a feature. Each feature should represent a
theme that emerged from a set of articles, so you might expect an article about a
new TV show to have a high weight for the word “television.”
The weights matrix
This matrix maps the features to the articles matrix. Each row is an article and
each column is a feature. The values state how much each feature applies to each
articl... (查看原文)
0 有用 千里驴 2011-05-09 22:24:53
深入浅出
0 有用 zzl 2012-07-31 10:54:03
机器学习入门好书,实践导向
0 有用 dt 2012-03-23 00:20:56
豆瓣的由来。。
0 有用 dimen61 2014-04-04 15:04:05
第一次看用代码表示数学公式的书,有一种码农造反的感觉。。
3 有用 贝塔 2008-11-10 14:08:13
这本书最大的好处就在于有最为具体的实例,让你想看不懂都难,想不知道怎么用都难。天下的书要是都能这么写,我也不至于这么文盲了。