Collaborative Filtering:Recommender Systems

by: Angshul Majumdar (Author)

Publisher: CRC Press

Edition: 1st

Publication Date: 2024/10/3

Language: English

Print Length: 127 pages

ISBN-10: 103284082X

ISBN-13: 9781032840826

Book Description

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 mode recommender systems.The jouey continues with exploring the concepts of metadata and diversity. You’ll discover how metadata, the additional information gathered by the system, can be haessed 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 leaing 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 leaing 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.

About the Author

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 mode recommender systems.The jouey continues with exploring the concepts of metadata and diversity. You’ll discover how metadata, the additional information gathered by the system, can be haessed 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 leaing 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 leaing 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.