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brief contents 1 ■ Meet Apache Mahout 1 PART 1 RECOMMENDATIONS ...................................................11 2 ■ Introducing recommenders 13 3 ■ Representing recommender data 26 4 ■ Making recommendations 41 5 ■ Taking recommenders to production 70 6 ■ Distributing recommendation computations 91 PART 2 CLUSTERING .............................................................115 7 ■ Introduction to clustering 117 8 ■ Representing data 130 9 ■ Clustering algorithms in Mahout 145 10 ■ Evaluating and improving clustering quality 184 11 ■ Taking clustering to production 198 12 ■ Real-world applications of clustering 210 Licensed to Jianbin Dai vi BRIEF CONTENTS PART 3 CLASSIFICATION ........................................................225 13 ■ Introduction to classification 227 14 ■ Training a classifier 255 15 ■ Evaluating and tuning a classifier 281 16 ■ Deploying a classifier 307 17 ■ Case study: Shop It To Me 341 Licensed to Jianbin Dai vii contents preface xvii acknowledgments xix about this book xx about multimedia extras xxiii about the cover illustration xxv 1 Meet Apache Mahout 1 1.1 Mahout’s story 2 1.2 Mahout’s machine learning themes 3 Recommender engines 3 ■ Clustering 3 ■ Classification 4 1.3 Tackling large scale with Mahout and Hadoop 5 1.4 Setting up Mahout 6 Java and IDEs 7 ■ Installing Maven 8 ■ Installing Mahout 8 ■ Installing Hadoop 9 1.5 Summary 9 PART 1 RECOMMENDATIONS...........................................11 2 Introducing recommenders 13 2.1 Defining recommendation 14 Licensed to Jianbin Dai viii CONTENTS 2.2 Running a first recommender engine 15 Creating the input 15 ■ Creating a recommender 16 Analyzing the output 17 2.3 Evaluating a recommender 18 Training data and scoring 18 ■ Running RecommenderEvaluator 19 ■ Assessing the result 20 2.4 Evaluating precision and recall 21 Running RecommenderIRStatsEvaluator 21 ■ Problems with precision and recall 23 2.5 Evaluating the

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