1st Edition

Collaborative Filtering Recommender Systems

By Angshul Majumdar Copyright 2025
141 Pages 10 B/W Illustrations
by CRC Press

141 Pages 10 B/W Illustrations
by CRC Press

141 Pages 10 B/W Illustrations
by CRC Press

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... Read more

Foreword

Preface

Author  

Chapter 1 Introduction and Organization 

1.1 Introduction

1.2 Contents of This Book

Chapter 2 Neighborhood-Based Models

2.1 Introduction

2.2 User-Based Approach

2.3 Item-Based Approach

Chapter 3 Ratings

3.1 Introduction

3.2 Biases and Baseline Correction 

3.3 Significance Weighting 

3.4 Optimally Learned Interpolation Weights 9vi Contents

Chapter 4 Latent Factor Models 

4.1 Introduction

4.2 Latent Factor Model

4.3 Nuclear Norm Minimization

Chapter 5 Using Metadata

5.1 Introduction

5.2 Matrix Factorization on Graphs 

5.3 Nuclear Norm Minimization on Multiple Graphs 

5.4 Label-Consistent Nuclear Norm Minimization 

5.5 Label-Consistent Matrix Factorization

Chapter 6 Diversity in Recommender Systems

6.1 Introduction 81

6.2 Prior Art

6.3 Matrix Factorization-Based Diversity Model

6.4 Nuclear Norm-Based Diversity Model

Chapter 7 Deep Latent Factor Models

7.1 Introduction

7.2 Brief Introduction to Representation Learning

7.3 Deep Latent Factor Model

7.4 Graphical Deep Latent Factor Model

7.5 Diversity in Deep Latent Factor Model

Chapter 8 Conclusion and Note to Instructors

8.1 Introduction

8.2 Course Organization

8.3. Expectation from Pupils

8.4 Evaluation

 

 

Biography

Angshul Majumdar is currently a professor at TCG CREST, Kolkata. Prior to that he was a professor at Indraprastha Institute of Information Technology, Delhi, India. He has been associated with the institute since 2012. Angshul did his Master’s (2009) and PhD (2012) in electrical and computer engineering from the University of British Columbia, Vancouver, Canada.

Angshul’s research interests lie in signal processing and machine learning with applications in smart grids and bioinformatics. Angshul has co-authored over 200 articles in journals and top tier conferences. He has written two books and co-edited two more and holds 7 US patents. He is an associate editor for IEEE Open Journal for Signal Processing and Elsevier Neurocomputing. In the past, he has been an associate editor for IEEE Transactions on Circuits and Systems for Video Technology.

Angshul is currently the director of student services at IEEE Signal Processing Society. Prior to that he was the chair for the education committee in the IEEE SPS membership board (2019). Angshul has also served as the chair for the chapter’s committee in the IEEE SPS membership board (2016-18). He had been the founding chair of IEEE SPS Delhi Chapter (2015-18). Angshul has been the organizing chair of two IEEE SPS Winter Schools in 2014 and 2017. He has served as the finance chair of IEEE ISBA 2017, the flagship conference of IEEE Biometrics Council.