MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02659nam a22003017a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
IIITD |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20250219165908.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
250218b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781032840826 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
IIITD |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
005.56 |
| Item number |
MAJ-C |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Majumdar, Angshul |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Collaborative filtering : |
| Remainder of title |
recommender systems |
| Statement of responsibility, etc |
by Angshul Majumdar |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
London : |
| Name of publisher, distributor, etc |
CRC Press, |
| Date of publication, distribution, etc |
©2025 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiii, 127 p. : |
| Other physical details |
ill. ; |
| Dimensions |
24 cm. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
1. Introduction and Organization |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
2. Neighborhood-Based Models |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
3. Ratings |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
4. Latent Factor Models |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
5. Using Metadata |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
6. Diversity in Recommender Systems |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
7. Deep Latent Factor Models |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
8. Conclusion and Note to Instructors |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Technology |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |
