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