Note: Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- 1 Introduction -- 1.1 Introduction to Recommender Systems -- 1.2 Formulation of the Recommendation Problem -- 1.2.1 The Input to a Recommender System -- 1.2.2 The Output of a Recommender System -- 1.3 Methods of Collecting Knowledge About User Preferences -- 1.3.1 The Implicit Approach -- 1.3.2 The Explicit Approach -- 1.3.3 The Mixing Approach -- 1.4 Motivation of the Book -- 1.5 Contribution of the Book -- 1.6 Outline of the Book -- References -- 2 Review of Previous Work Related to Recommender Systems -- 2.1 Content-Based Methods -- 2.2 Collaborative Methods -- 2.2.1 User-Based Collaborative Filtering Systems -- 2.2.2 Item-Based Collaborative Filtering Systems -- 2.2.3 Personality Diagnosis -- 2.3 Hybrid Methods -- 2.3.1 Adding Content-Based Characteristics to Collaborative Models -- 2.3.2 Adding Collaborative Characteristics to Content-Based Models -- 2.3.3 A Single Unifying Recommendation Model -- 2.3.4 Other Types of Recommender Systems -- 2.4 Fundamental Problems of Recommender Systems -- References -- 3 The Learning Problem -- 3.1 Introduction -- 3.2 Types of Learning -- 3.3 Statistical Learning -- 3.3.1 Classical Parametric Paradigm -- 3.3.2 General Nonparametric---Predictive Paradigm -- 3.3.3 Transductive Inference Paradigm -- 3.4 Formulation of the Learning Problem -- 3.5 The Problem of Classification -- 3.5.1 Empirical Risk Minimization -- 3.5.2 Structural Risk Minimization -- 3.6 Support Vector Machines -- 3.6.1 Basics of Support Vector Machines -- 3.6.2 Multi-class Classification Based on SVM -- 3.7 One-Class Classification -- 3.7.1 One-Class SVM Classification -- 3.7.2 Recommendation as a One-Class Classification Problem -- References -- 4 Content Description of Multimedia Data -- 4.1 Introduction -- 4.2 MPEG-7 -- 4.2.1 Visual Content Descriptors. , 4.2.2 Audio Content Descriptors -- 4.3 MARSYAS: Audio Content Features -- 4.3.1 Music Surface Features -- 4.3.2 Rhythm Features and Tempo -- 4.3.3 Pitch Features -- References -- 5 Similarity Measures for Recommendations Based on Objective Feature Subset Selection -- 5.1 Introduction -- 5.2 Objective Feature-Based Similarity Measures -- 5.3 Architecture of MUSIPER -- 5.4 Incremental Learning -- 5.5 Realization of MUSIPER -- 5.5.1 Computational Realization of Incremental Learning -- 5.6 MUSIPER Operation Demonstration -- 5.7 MUSIPER Evaluation Process -- 5.8 System Evaluation Results -- References -- 6 Cascade Recommendation Methods -- 6.1 Introduction -- 6.2 Cascade Content-Based Recommendation -- 6.3 Cascade Hybrid Recommendation -- 6.4 Measuring the Efficiency of the Cascade Classification Scheme -- References -- 7 Evaluation of Cascade Recommendation Methods -- 7.1 Introduction -- 7.2 Comparative Study of Recommendation Methods -- 7.3 One-Class SVM---Fraction: Analysis -- 8 Conclusions and Future Work -- 8.1 Summary and Conclusions -- 8.2 Current and Future Work.

Note: Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- Part I Machine Learning Fundamentals -- 1 Introduction -- References -- 2 Machine Learning -- 2.1 Introduction -- 2.2 Machine Learning Categorization According to the Type of Inference -- 2.2.1 Model Identification -- 2.2.2 Shortcoming of the Model Identification Approach -- 2.2.3 Model Prediction -- 2.3 Machine Learning Categorization According to the Amount of Inference -- 2.3.1 Rote Learning -- 2.3.2 Learning from Instruction -- 2.3.3 Learning by Analogy -- 2.4 Learning from Examples -- 2.4.1 The Problem of Minimizing the Risk Functional from Empirical Data -- 2.4.2 Induction Principles for Minimizing the Risk Functional on Empirical Data -- 2.4.3 Supervised Learning -- 2.4.4 Unsupervised Learning -- 2.4.5 Reinforcement Learning -- 2.5 Theoretical Justifications of Statistical Learning Theory -- 2.5.1 Generalization and Consistency -- 2.5.2 Bias-Variance and Estimation-Approximation Trade-Off -- 2.5.3 Consistency of Empirical Minimization Process -- 2.5.4 Uniform Convergence -- 2.5.5 Capacity Concepts and Generalization Bounds -- 2.5.6 Generalization Bounds -- References -- 3 The Class Imbalance Problem -- 3.1 Nature of the Class Imbalance Problem -- 3.2 The Effect of Class Imbalance on Standard Classifiers -- 3.2.1 Cost Insensitive Bayes Classifier -- 3.2.2 Bayes Classifier Versus Majority Classifier -- 3.2.3 Cost Sensitive Bayes Classifier -- 3.2.4 Nearest Neighbor Classifier -- 3.2.5 Decision Trees -- 3.2.6 Neural Networks -- 3.2.7 Support Vector Machines -- References -- 4 Addressing the Class Imbalance Problem -- 4.1 Resampling Techniques -- 4.1.1 Natural Resampling -- 4.1.2 Random Over-Sampling and Random Under-Sampling -- 4.1.3 Under-Sampling Methods -- 4.1.4 Over-Sampling Methods -- 4.1.5 Combination Methods -- 4.2 Cost Sensitive Learning -- 4.2.1 The MetaCost Algorithm. , 4.3 One Class Learning -- 4.3.1 One Class Classifiers -- 4.3.2 Density Models -- 4.3.3 Boundary Methods -- 4.3.4 Reconstruction Methods -- 4.3.5 Principal Components Analysis -- 4.3.6 Auto-Encoders and Diabolo Networks -- References -- 5 Machine Learning Paradigms -- 5.1 Support Vector Machines -- 5.1.1 Hard Margin Support Vector Machines -- 5.1.2 Soft Margin Support Vector Machines -- 5.2 One-Class Support Vector Machines -- 5.2.1 Spherical Data Description -- 5.2.2 Flexible Descriptors -- 5.2.3 v - SVC -- References -- Part II Artificial Immune Systems -- 6 Immune System Fundamentals -- 6.1 Introduction -- 6.2 Brief History and Perspectives on Immunology -- 6.3 Fundamentals and Main Components -- 6.4 Adaptive Immune System -- 6.5 Computational Aspects of Adaptive Immune System -- 6.5.1 Pattern Recognition -- 6.5.2 Immune Network Theory -- 6.5.3 The Clonal Selection Principle -- 6.5.4 Immune Learning and Memory -- 6.5.5 Immunological Memory as a Sparse Distributed Memory -- 6.5.6 Affinity Maturation -- 6.5.7 Self/Non-self Discrimination -- References -- 7 Artificial Immune Systems -- 7.1 Definitions -- 7.2 Scope of AIS -- 7.3 A Framework for Engineering AIS -- 7.3.1 Shape-Spaces -- 7.3.2 Affinity Measures -- 7.3.3 Immune Algorithms -- 7.4 Theoretical Justification of the Machine Learning -- 7.5 AIS-Based Clustering -- 7.5.1 Background Immunological Concepts -- 7.5.2 The Artificial Immune Network (AIN) Learning Algorithm -- 7.5.3 AiNet Characterization and Complexity Analysis -- 7.6 AIS-Based Classification -- 7.6.1 Background Immunological Concepts -- 7.6.2 The Artificial Immune Recognition System (AIRS) Learning Algorithm -- 7.6.3 Source Power of AIRS Learning Algorithm and Complexity Analysis -- 7.7 AIS-Based Negative Selection -- 7.7.1 Background Immunological Concepts -- 7.7.2 Theoretical Justification of the Negative Selection Algorithm. , 7.7.3 Real-Valued Negative Selection with Variable-Sized Detectors -- 7.7.4 AIS-Based One-Class Classification -- 7.7.5 V-Detector Algorithm -- References -- 8 Experimental Evaluation of Artificial Immune System-Based Learning Algorithms -- 8.1 Experimentation -- 8.1.1 The Test Data Set -- 8.1.2 Artificial Immune System-Based Music Piece Clustering and Database Organization -- 8.1.3 Artificial Immune System-Based Customer Data Clustering in an e-Shopping Application -- 8.1.4 AIS-Based Music Genre Classification -- 8.1.5 Music Recommendation Based on Artificial Immune Systems -- References -- 9 Conclusions and Future Work.