Collaborative Filtering: Recommender Systems [Hardcover]

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

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.

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 Masters (2009) and PhD (2012) in electrical and computer engineering from the University of British Columbia, Vancouver, Canada.

Angshuls 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 topl³Ï