Collaborative filtering: Recommender systems

Abstract

This book dives into the inner workings of recommender systems, those ubiquitous technologies that shape our online experiences. From Netfix 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 fltering 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 fltering. 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 refne 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 feld. Second, the book can be used as a textbook for a graduate-level course. To facilitate this, the fnal chapter provides instructors with a potential course plan.