Ulteriori informazioni
Explains methods behind machine learning systems to personalize predictions to individual users, from recommendation to dating and fashion.
Sommario
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.
Info autore
Julian McAuley has been a Professor at UC San Diego since 2014. Personalized Machine Learning is the main research area of his lab, with applications ranging from personalized recommendation, to dialog, healthcare, and fashion design. He regularly collaborates with industry on these topics, including with Amazon, Facebook, Microsoft, Salesforce, and Etsy. His work has been selected for several awards, including an NSF CAREER award, and faculty awards from Amazon, Salesforce, Facebook, and Qualcomm, among others.
Riassunto
For practitioners and students with basic understanding of machine learning or data science, this book explains how to build models or predictive systems involving user data. Examples range from the algorithms behind recommendations on Amazon or Netflix to more complex scenarios such as personalized fashion, online dating, or personalized health.
Prefazione
Explains methods behind machine learning systems to personalize predictions to individual users, from recommendation to dating and fashion.
Testo aggiuntivo
'A comprehensive, authoritative, and systematic introduction to personalized machine learning. Starting with essential concepts on machine learning, the book covers multiple architectures of recommender systems as well as personalized models of text and visual data. A great book for both new learners and advanced researchers!' Jiawei Han, Michael-Aiken Chair Professor, University of Illinois at Urbana-Champaign
