| ISBN13: | 9781032840826 |
| ISBN10: | 103284082X |
| Binding: | Hardback |
| No. of pages: | 141 pages |
| Size: | 234x156 mm |
| Weight: | 421 g |
| Language: | English |
| Illustrations: | 10 Illustrations, black & white; 10 Line drawings, black & white |
| 684 |
Library and information science in general
Electrical engineering and telecommunications, precision engineering
Energy industry
Theory of computing, computing in general
Computer networks in general
Database management softwares
Environmental sciences
Library and information science in general (charity campaign)
Electrical engineering and telecommunications, precision engineering (charity campaign)
Energy industry (charity campaign)
Theory of computing, computing in general (charity campaign)
Computer networks in general (charity campaign)
Database management softwares (charity campaign)
Environmental sciences (charity campaign)
Collaborative Filtering
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This book dives into the inner workings of recommender systems, those ubiquitous technologies that shape our online experiences. From Netflix show suggestions to personalized product recommendations on Amazon or the endless stream of curated YouTube videos, these systems power the choices we see every day.
This book dives into the inner workings of recommender systems, those ubiquitous technologies that shape our online experiences. From Netflix show suggestions to personalized product recommendations on Amazon or the endless stream of curated YouTube videos, these systems power the choices we see every day.
Collaborative filtering reigns supreme as the dominant approach behind recommender systems. This book offers a comprehensive exploration of this topic, starting with memory-based techniques. These methods, known for their ease of understanding and implementation, provide a solid foundation for understanding collaborative filtering. As you progress, you?ll delve into latent factor models, the abstract and mathematical engines driving modern recommender systems.
The journey continues with exploring the concepts of metadata and diversity. You?ll discover how metadata, the additional information gathered by the system, can be harnessed to refine recommendations. Additionally, the book delves into techniques for promoting diversity, ensuring a well-balanced selection of recommendations. Finally, the book concludes with a discussion of cutting-edge deep learning models used in recommender systems.
This book caters to a dual audience. First, it serves as a primer for practicing IT professionals or data scientists eager to explore the realm of recommender systems. The book assumes a basic understanding of linear algebra and optimization but requires no prior knowledge of machine learning or programming. This makes it an accessible read for those seeking to enter this exciting field. Second, the book can be used as a textbook for a graduate-level course. To facilitate this, the final chapter provides instructors with a potential course plan.
Key features:
? This is the only book covering 25 years of research on this topic starting from late 90s to the current year.
? This book is accessible to anyone with a basic knowledge of linear algebra, unlike other volumes that require knowledge of advanced data analytics.
? It covers a wider range of topics than other books. Most others are research oriented and delves deep into a narrow area.
? This is the only book written to be a textbook on collaborative filtering and recommender systems.
? The book emphasizes on algorithms and not implementation. This makes it agnostic to programming languages. The reader is free to use whatever they are comfortable in, such as Python, R, Matlab, Java, etc.
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
