Collaborative Filtering: Recommender Systems
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- Synopsis
- 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.
- Copyright:
- 2025
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9781040123522
- Related ISBNs:
- 9781032840826, 9781003511267, 9781040123461
- Publisher:
- CRC Press
- Date of Addition:
- 10/02/24
- Copyrighted By:
- Angshul Majumdar. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have atte
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Technology
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.