McAuley J. Personalized Machine Learning

Cambridge: Cambridge University Press, 2022. — 337 p.Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.FrontmatterMachine Learning Primer
Regression and Feature Engineering
Classification and the Learning Pipeline
Fundamentals of Personalized Machine Learning
Introduction to Recommender Systems
Model-Based Approaches to Recommendation
Content and Structure in Recommender Systems
Temporal and Sequential Models
Emerging Directions in Personalized Machine Learning
Personalized Models of Text
Personalized Models of Visual Data
The Consequences of Personalized Machine Learning