1st Edition

Collaborative Filtering Recommender Systems

By Angshul Majumdar Copyright 2025
    152 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 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. Firstly, 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.Secondly, 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.




    Chapter 1: Introduction and Organization


    Content of this book

    Chapter 2: Neighborhood Based Models


    User-based Approach

    Item-based Approach


    Chapter 3: Ratings


    Biases and Baseline Correction

    Significance Weighting

    Optimally Learnt Interpolation Weights


    Chapter 4: Latent Factor Models


    Latent Factor Model

    Nuclear Norm Minimization


    Chapter 5: Using Metadata


    Matrix Factorization on Graphs

    Nuclear norm minimization on Multiple Graphs

    Label consistent nuclear norm minimization

    Label consistent matrix factorization



    Chapter 6: Diversity in Recommender Systems


    Prior Art

    Matrix Factorization based Diversity Model

    Nuclear Norm-based Diversity Model


    Chapter 7: Deep Latent Factor Models


    Brief Introduction to Representation Learning

    Deep Latent Factor Model

    Graphical Deep Latent Factor Model

    Diversity in Deep Latent Factor Model


    Chapter 8: Conclusion & Note to Instructors


    Course Organization

    Expectation from pupils






    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.