Collaborative Filtering

Chapter 1 Introduction and Organization 1.1 Introduction 1.2 Contents of This Book Chapter 2 Neighborhood-Based Models 2.1 Introduction 2.2 User-Based Approach 2.3 Item-Based Approach Chapter 3 Ratings 3.1 Introduction 3.2 Biases and Baseline Correction 3.3 Significance Weighting 3.4 Optimally Learned Interpolation Weights Chapter 4 Latent Factor Models 4.1 Introduction 4.2 Latent Factor Model 4.3 Nuclear Norm Minimization Chapter 5 Using Metadata 5.1 Introduction 6.2 Prior Art 6.3 Matrix Factorization-Based Diversity Model 6.4 Nuclear Dorm-Based Diversity Model Chapter 7 Deep Latent Factor Models 7.1 Introduction 7.2 Brief Introduction to Representation Learning 7.3 Deep Latent Factor Model 7.4 Graphical Deep Latent Factor Model 7.5 Diversity in Deep Latent Factor Model Chapter 8 Conclusion and Note to Instructors 8.1 Introduction 8.2 Course Organization 8.3. Expectation from Pupils 8.4 Evaluation

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• Ciências da biblioteca e da informação
• Ciência da Computação
• Ciência ambiental, engenharia e tecnologia
• Engenharia electrotécnica
• Engenharia das comunicações e telecomunicações
• Bases de Dados
• Tecnologia da informação: aspectos gerais
• Gestão de rede
• Engenharia electrónica

Collaborative;Filtering;Recommender;Systems