Angshul Majumdar

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Recommender Systems

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 141
• Erscheinungstermin: 3. Oktober 2024
• Englisch
• ISBN-13: 9781040123461
• Artikelnr.: 72273225

Autorenporträt

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.

Inhaltsangabe

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 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 6.2 Prior Art 6.3 Matrix
Factorization-Based Diversity Model 6.4 Nuclear Dorm-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