Table of Contents

 

Copyright

Brief Table of Contents

Table of Contents

Preface

Acknowledgments

About this Book

About Multimedia Extras

About the Cover Illustration

Chapter 1. Meet Apache Mahout

1.1. Mahout’s story

1.2. Mahout’s machine learning themes

1.2.1. Recommender engines

1.2.2. Clustering

1.2.3. Classification

1.3. Tackling large scale with Mahout and Hadoop

1.4. Setting up Mahout

1.4.1. Java and IDEs

1.4.2. Installing Maven

1.4.3. Installing Mahout

1.4.4. Installing Hadoop

1.5. Summary

1. Recommendations

Chapter 2. Introducing recommenders

2.1. Defining recommendation

2.2. Running a first recommender engine

2.2.1. Creating the input

2.2.2. Creating a recommender

2.2.3. Analyzing the output

2.3. Evaluating a recommender

2.3.1. Training data and scoring

2.3.2. Running RecommenderEvaluator

2.3.3. Assessing the result

2.4. Evaluating precision and recall

2.4.1. Running RecommenderIRStatsEvaluator

2.4.2. Problems with precision and recall

2.5. Evaluating the GroupLens data set

2.5.1. Extracting the recommender input

2.5.2. Experimenting with other recommenders

2.6. Summary

Chapter 3. Representing recommender data

3.1. Representing preference data

3.1.1. The Preference object

3.1.2. PreferenceArray and implementations

3.1.3. Speeding up collections

3.1.4. FastByIDMap and FastIDSet

3.2. In-memory DataModels

3.2.1. GenericDataModel