Part 1 Recommendations

Chapter 2 Introducing recommenders

Defining recommendation

Running a first recommender engine

Evaluating a recommender

Evaluating precision and recall

Evaluating the GroupLens data set

Summary

Chapter 3 Representing recommender data

Representing preference data

In-memory DataModels

Coping without preference values

Summary

Chapter 4 Making recommendations

Understanding user-based recommendation

Exploring the user-based recommender

Exploring similarity metrics

Item-based recommendation

Slope-one recommender

New and experimental recommenders

Comparison to other recommenders

Summary

Chapter 5 Taking recommenders to production

Analyzing example data from a dating site

Finding an effective recommender

Injecting domain-specific information

Recommending to anonymous users

Creating a web-enabled recommender

Updating and monitoring the recommender

Summary

Chapter 6 Distributing recommendation computations

Analyzing the Wikipedia data set

Designing a distributed item-based algorithm

Implementing a distributed algorithm with MapReduce

Running MapReduces with Hadoop

Pseudo-distributing a recommender

Looking beyond first steps with recommendations

Summary

Part 2 Clustering

Chapter 7 Introduction to clustering

Clustering basics

Measuring the similarity of items

Hello World: running a simple clustering example

Exploring distance measures

Hello World again! Trying out various distance measures

Summary

Chapter 8 Representing data

Visualizing vectors

Representing text documents as vectors

Generating vectors from documents

Improving quality of vectors using normalization

Summary

Chapter 9 Clustering algorithms in Mahout

K-means clustering

Beyond k-means: an overview of clustering techniques

Fuzzy k-means clustering

Model-based clustering

Topic modeling using latent Dirichlet allocation (LDA)

Summary

Chapter 10 Evaluating and improving clustering quality

Inspecting clustering output

Analyzing clustering output

Improving clustering quality

Summary

Chapter 11 Taking clustering to production

Quick-start tutorial for running clustering on Hadoop

Tuning clustering performance

Batch and online clustering

Summary

Chapter 12 Real-world applications of clustering

Finding similar users on Twitter

Suggesting tags for artists on Last.fm

Analyzing the Stack Overflow data set

Summary

Part 3 Classification

Chapter 13 Introduction to classification

Why use Mahout for classification?

The fundamentals of classification systems

How classification works

Work flow in a typical classification project

Step-by-step simple classification example

Summary

Chapter 14 Training a classifier

Extracting features to build a Mahout classifier

Preprocessing raw data into classifiable data

Converting classifiable data into vectors

Classifying the 20 newsgroups data set with SGD

Choosing an algorithm to train the classifier

Classifying the 20 newsgroups data with naive Bayes

Summary

Chapter 15 Evaluating and tuning a classifier

Classifier evaluation in Mahout

The classifier evaluation API

When classifiers go bad

Tuning for better performance

Summary

Chapter 16 Deploying a classifier

Process for deployment in huge systems

Determining scale and speed requirements

Building a training pipeline for large systems

Integrating a Mahout classifier

Example: a Thrift-based classification server

Summary

Chapter 17 Case study: Shop It To Me

Why Shop It To Me chose Mahout

General structure of the email marketing system

Training the model

Speeding up classification

Summary

appendix A JVM tuning
appendix B Mahout math
appendix C Resources
index