Personalized Machine Learning

Explains methods behind machine learning systems to personalize predictions to individual users, from recommendation to dating and fashion.

Julian McAuley (Author)

9781316518908, Cambridge University Press

Hardback, published 3 February 2022

350 pages
23.5 x 15.9 x 2.1 cm, 0.6 kg

'An authority in this relatively new field, McAuley offers a valuable and timely course textbook … In addition to its use in information and computer science coursework, it will appeal to all readers interested in personal aspects of digital technology and user experience … Recommended.' C. Tappert, Choice

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.

1. Introduction
Part I. Machine Learning Primer: 2. Regression and feature engineering
3. Classification and the learning pipeline
Part II. Fundamentals of Personalized Machine Learning: 4. Introduction to recommender systems
5. Model-based approaches to recommendation
6. Content and structure in recommender systems
7. Temporal and sequential models
Part III. Emerging Directions in Personalized Machine Learning: 8. Personalized models of text
9. Personalized models of visual data
10. The consequences of personalized machine learning
References
Index.

Subject Areas: Machine learning [UYQM], Natural language & machine translation [UYQL], Databases [UN], Knowledge management [KJMV3]