掌握集体智能编程：构建Web 2.0应用与数据分析

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《编程集体智能》是一本深度探讨如何在Web 2.0环境中利用数据挖掘技术来提升搜索排名、产品推荐、社交书签和在线匹配功能的实用指南。该书由Toby介绍了众多先进的算法和方法，将读者引入机器学习和统计学的世界，通过这些技术，开发者能够构建出智能程序，访问其他网站的有趣数据集，收集用户应用数据，并对获取的数据进行分析和理解。书中核心知识点包括： 1. **协作过滤技术**：作者展示了如何运用这种技术，让在线零售商根据用户的购买历史或行为习惯推荐个性化的产品或媒体，提升用户体验。 2. **聚类方法**：通过大数据集中的相似性检测，识别出具有共同特征的物品群体，这对于内容管理和市场细分非常有价值。 3. **搜索引擎功能**：涵盖了爬虫、索引器、查询引擎以及PageRank算法，让读者了解搜索引擎如何抓取、组织和评估网页的重要性。 4. **优化算法**：介绍用于在大量解决方案中寻找最优解的算法，如遗传算法或模拟退火，这在解决复杂问题时尤为关键。 5. **贝叶斯过滤**：应用于垃圾邮件过滤，通过分析文档中的词型和其他特征，对信息进行分类和过滤。 6. **决策树**：不仅用于预测，还能模拟决策过程，帮助理解和优化用户行为决策。 7. **数值预测**：书中涉及如何构建价格模型，通过对数值数据的预测来提高商业决策的准确性。 8. **支持向量机（SVM）**：在在线约会网站中，SVM用于匹配用户，基于用户的兴趣和偏好进行匹配。 9. **非负矩阵分解**：这种方法用于揭示数据集中独立的特征，有助于深入理解数据结构。 10. **演化智能**：书中还探讨了计算机如何通过自我改进代码来提升解决问题的能力，例如在游戏中学习和适应。每一章都包含实践性的练习，引导读者扩展算法，使其更具功能，超越传统的数据库应用程序，利用互联网上的海量数据为己所用。这本书深受业内专家的好评，无论是新手还是经验丰富的AI从业者，都能从中获得宝贵的知识和启发，避免在探索机器学习算法时走弯路。

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Preface

implement one may use a different paradigm than another. Sometimes it’s use-

ful to pass around functions as parameters and other times to capture state in an

object. Python supports both approaches.

Multiplatform and free

Python has a single reference implementation for all the major platforms and is

free for all of them. The code described in this book will work on Windows,

Linux, and Macintosh.

Python Tips

For beginners interested in learning about programming in Python, I recommend

reading Learning Python by Mark Lutz and David Ascher (O’Reilly), which gives an

excellent overview. Programmers of other languages should find the Python code rel-

atively easy to follow, although be aware that throughout this book I use some of

Python’s idiosyncratic syntax because it lets me more directly express the algorithm

or fundamental concepts. Here’s a quick overview for those of you who aren’t

Python programmers:

List and dictionary constructors

Python has a good set of primitive types and two that are used heavily throughout

this book are list and dictionary. A list is an ordered list of any type of value, and it is

constructed with square brackets:

number_list=[1,2,3,4]

string_list=['a', 'b', 'c', 'd']

mixed_list=['a', 3, 'c', 8]

A dictionary is an unordered set of key/value pairs, similar to a hash map in other

languages. It is constructed with curly braces:

ages={'John':24,'Sarah':28,'Mike':31}

The elements of lists and dictionaries can be accessed using square brackets after the

list name:

string_list[2] # returns 'b'

ages['Sarah'] # returns 28

Signiﬁcant Whitespace

Unlike most languages, Python actually uses the indentation of the code to define

code blocks. Consider this snippet:

if x==1:

print 'x is 1'

print 'Still in if block'

print 'outside if block'

Preface

xvii

del.icio.us

A social bookmarking application whose open API lets you download links by

tag or from a specific user.

Kayak

A travel site with an API for conducting searches for flights and hotels from

within your own programs.

eBay

An online auction site with an API that allows you to query items that are cur-

rently for sale.

Hot or Not

A rating and dating site with an API to search for people and get their ratings

and demographic information.

Akismet

An API for collaborative spam filtering.

A huge number of potential applications can be built by processing data from a

single source, by combining data from multiple sources, and even by combining

external information with input from your own users. The ability to harness data cre-

ated by people in a variety of ways on different sites is a principle element of creating

collective intelligence. A good starting point for finding more web sites with open

APIs is ProgrammableWeb (http://www.programmableweb.com).

Overview of the Chapters

Every algorithm in the book is motivated by a realistic problem that can, I hope, be

easily understood by all readers. I have tried to avoid problems that require a great

deal of domain knowledge, and I have focused on problems that, while complex, are

easy for most people to relate to.

Chapter 1, Introduction to Collective Intelligence

Explains the concepts behind machine learning, how it is applied in many differ-

ent fields, and how it can be used to draw new conclusions from data gathered

from many different people.

Chapter 2, Making Recommendations

Introduces the collaborative filtering techniques used by many online retailers to

recommend products or media. The chapter includes a section on recommend-

ing links to people from a social bookmarking site, and building a move

recommendation system from the MovieLens dataset.

Chapter 3, Discovering Groups

Builds on some of the ideas in Chapter 2 and introduces two different methods

of clustering, which automatically detect groups of similar items in a large

dataset. This chapter demonstrates the use of clustering to find groups on a set

of popular weblogs and on people’s desires from a social networking web site.

xviii

Preface

Chapter 4, Searching and Ranking

Describes the various parts of a search engine including the crawler, indexer, and

query engine. It covers the PageRank algorithm for scoring pages based on

inbound links and shows you how to create a neural network that learns which

keywords are associated with different results.

Chapter 5, Optimization

Introduces algorithms for optimization, which are designed to search millions of

possible solutions to a problem and choose the best one. The wide variety of

uses for these algorithms is demonstrated with examples that find the best flights

for a group of people traveling to the same location, find the best way of match-

ing students to dorms, and lay out a network with the minimum number of

crossed lines.

Chapter 6, Document Filtering

Demonstrates Bayesian filtering, which is used in many free and commercial

spam filters for automatically classifying documents based on the type of words

and other features that appear in the document. This is applied to a set of RSS

search results to demonstrate automatic classification of the entries.

Chapter 7, Modeling with Decision Trees

Introduces decision trees as a method not only of making predictions, but also of

modeling the way the decisions are made. The first decision tree is built with

hypothetical data from server logs and is used to predict whether or not a user is

likely to become a premium subscriber. The other examples use data from real

web sites to model real estate prices and “hotness.”

Chapter 8, Building Price Models

Approaches the problem of predicting numerical values rather than classifica-

tions using k-nearest neighbors techniques, and applies the optimization

algorithms from Chapter 5. These methods are used in conjunction with the

eBay API to build a system for predicting eventual auction prices for items based

on a set of properties.

Chapter 9, Advanced Classification: Kernel Methods and SVMs

Shows how support-vector machines can be used to match people in online dat-

ing sites or when searching for professional contacts. Support-vector machines

are a fairly advanced technique and this chapter compares them to other methods.

Chapter 10, Finding Independent Features

Introduces a relatively new technique called non-negative matrix factorization,

which is used to find the independent features in a dataset. In many datasets the

items are constructed as a composite of different features that we don’t know in

advance; the idea here is to detect these features. This technique is demon-

strated on a set of news articles, where the stories themselves are used to detect

themes, one or more of which may apply to a given story.

剩余359页未读，继续阅读

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