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Collaborative filtering : recommender systems

By: Material type: TextTextPublication details: London : CRC Press, ©2025Description: xiii, 127 p. : ill. ; 24 cmISBN:
  • 9781032840826
Subject(s): DDC classification:
  • 005.56 MAJ-C
Contents:
1. Introduction and Organization
2. Neighborhood-Based Models
3. Ratings
4. Latent Factor Models
5. Using Metadata
6. Diversity in Recommender Systems
7. Deep Latent Factor Models
8. Conclusion and Note to Instructors
Summary: 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.
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Holdings
Item type Current library Collection Call number Status Notes Date due Barcode Item holds
Books Books IIITD General Stacks Computer Science and Engineering 005.56 MAJ-C (Browse shelf(Opens below)) Available Gifted by Dr. Angshul Majumdar G02793
Books Books IIITD Reference Computer Science and Engineering REF 005.56 MAJ-C (Browse shelf(Opens below)) Not for loan Gifted by Dr. Angshul Majumdar G02794
Total holds: 0
Browsing IIITD shelves, Shelving location: Reference, Collection: Computer Science and Engineering Close shelf browser (Hides shelf browser)
REF 005.453 MUC-A Advanced compiler design and implementation REF 005.453 SRI-C The compiler design handbook : REF 005.456 LEV-L Linkers and loaders REF 005.56 MAJ-C Collaborative filtering : recommender systems REF 005.7 BAE-M Modern information retrieval REF 005.7 BAE-M Modern information retrieval : REF 005.7 BUT-I Information retrieval :

Includes index.

1. Introduction and Organization

2. Neighborhood-Based Models

3. Ratings

4. Latent Factor Models

5. Using Metadata

6. Diversity in Recommender Systems

7. Deep Latent Factor Models

8. Conclusion and Note to Instructors

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.

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