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资源说明：Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
1. Introduction to Collective Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
What Is Collective Intelligence?
What Is Machine Learning?
Limits of Machine Learning
Real-Life Examples
Other Uses for Learning Algorithms
2
3
4
5
5
2. Making Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
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
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8
9
15
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19
22
25
27
28
3. Discovering Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Supervised versus Unsupervised Learning
Word Vectors
Hierarchical Clustering
Drawing the Dendrogram
Column Clustering
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30
33
38
40
vii
K-Means Clustering
Clusters of Preferences
Viewing Data in Two Dimensions
Other Things to Cluster
Exercises
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44
49
53
53
4. Searching and Ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
What’s in a Search Engine?
A Simple Crawler
Building the Index
Querying
Content-Based Ranking
Using Inbound Links
Learning from Clicks
Exercises
54
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58
63
64
69
74
84
5. Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
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
87
88
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91
92
95
97
101
106
110
115
116
6. Document Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Filtering Spam
Documents and Words
Tra
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