Название: Collaborative Filtering: Recommender Systems
Автор: Angshul Majumdar
Издательство: CRC Press
Год: 2025
Страниц: 142
Язык: английский
Формат: pdf (true)
Размер: 14.2 MB

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.

Recommender systems, powered by sophisticated algorithms and Machine Learning techniques, analyze user behavior, preferences, and past consumption patterns to identify patterns and predict future choices. These systems act as digital surrogates for the trusted friends, family, and experts who traditionally guided consumer decisions in the pre-internet era.

Recommender systems are continuously evolving, incorporating new data sources, algorithms, and Machine Learning techniques to improve their accuracy and personalization. As these systems become more sophisticated, they will play an even more integral role in shaping consumer behavior and infuencing purchasing decisions.

In the realm of recommender systems, two distinct approaches have emerged: content-based fltering and collaborative fltering. While both aim to provide personalized recommendations to users, they differ signifcantly in their underlying principles and the data they utilize.

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.