想要探寻搜索排名、产品推荐、社会化书签和在线匹配背后的力量吗？这本颇具魅力的书籍向你展现如何创建Web 2.0应用程序，从参与性Internet应用程序产生的大量数据中挖掘金矿。运用本书中介绍的先进算法，你可以编写聪明的程序，以访问其他网站那些有趣的数据集，从自有应用程序的用户中收集数据，或者分析和理解你所发现的数据。

《集体智慧编程》将你带入机器学习和统计的世界，并且阐释了如何从你和他人每天收集的信息中获得关于用户体验、市场营销、个性品味及人类行为的结论。每个算法的描述都十分简明清晰，相关代码均可以立即用于你的网站、博客、Wiki或特定应用程序。本书讲解了下列主题：

可以让在线零售商推荐产品或媒体的协作过滤技术；用于在大数据集中发现同类项组的聚类方法；从数以百万计可能方案中选择问题最佳解决方案的最优化算法；贝叶斯过滤，用在基于单词类型和其他特征的垃圾信息过滤中；支持向量（support-vector）机器，用于在线交友网站中的速配；用于问题解决的演化智能——计算机如何通过多次玩同样的游戏，改进自身代码并获得技能提升。

每一章都包含了相关练习，可通过扩展使算法变得更强大。超越简单的数据库支持应用程序模式，让 Internet数据财富为你所用。

Foreword

Preface

1. Introduction to Collective Intelligence

What Is Collective Intelligence?

What Is Machine Learning?

Limits of Machine Learning

Real-Life Examples

Other Uses for Learning Algorithms

2. Making Recommendations

Collaborative Filtering

Collecting Preferences

Finding Similar Users

Recommending Items

Matching Products

Building a del.icio.us Link Recommender

Item-Based Filtering

Using the MovieLens Dataset

User-Based or Item-Based Filtering?

Exercises

3. Discovering Groups

Supervised versus Unsupervised Learning

Word Vectors

Hierarchical Clustering

Drawing the Dendrogram

Column Clustering

K-Means Clustering

Clusters of Preferences

Viewing Data in Two Dimensions

Other Things to Cluster

Exercises

4. Searching and Ranking

What's in a Search Engine?

A Simple Crawler

Building the Index

Querying

Content-Based Ranking,

Using Inbound Links

Learning from Clicks

Exercises

5. Optimization

Group Travel

Representing Solutions

The Cost Function

Random Searching

Hill Climbing

Simulated Annealing

Genetic Algorithms

Real Flight Searches

Optimizing for Preferences

Network Visualization

Other Possibilities

Exercises

6. Document Filtering

Filtering Spam

Documents and Words

Training the Classifier

Calculating Probabilities

A Naive Classifier

The Fisher Method

Persisting the Trained Classifiers

Filtering Blog Feeds

Improving Feature Detection

Using Akismet

Alternative Methods

Exercises

7. Modeling with Decision Trees

Predicting Signups

Introducing Decision Trees

Training the Tree

Choosing the Best Split

Recursive Tree Building

Displaying the Tree

Classifying New Observations

Pruning the Tree

Dealing with Missing Data

Dealing with Numerical Outcomes

Modeling Home Prices

Modeling "Hotness"

When to Use Decision Trees

Exercises

8. Building Price Models

Building a Sample Dataset

k-Nearest Neighbors

Weighted Neighbors

Cross-Validation

Heterogeneous Variables

Optimizing the Scale

Uneven Distributions

Using Real Data--the eBay API

When to Use k-Nearest Neighbors

Exercises

9. Advanced Classification: Kernel Methods and SVMs

Matchmaker Dataset

Difficulties with the Data

Basic Linear Classification

CateRorical Features

Scaling the Data

Understanding Kernel Methods

Support-Vector Machines

Using LIBSVM

Matching on Facebook

Exercises

10. Finding Independent Features

A Corpus of News

Previous Approaches

Non-Negative Matrix Factorization

Displaying the Results

Using Stock Market Data

Exercises

11. Evolving Intelligence

What Is Genetic Programming?

Programs As Trees

Creating the Initial Population

Testing a Solution

Mutating Programs

Crossover

Building the Environment

A Simple Game

Further Possibilities

Exercises

12. Algorithm Summary

Bayesian Classifier

Decision Tree Classifier

Neural Networks

Support-Vector Machines

k-Nearest Neighbors

Clustering

Multidimensional Scaling

Non-Negative Matrix Factorization

Optimization

A. Third-Party Libraries

B. MathematicaIFormulas

Index