Personalized Machine Learning

Rezensionen / Stimmen

'This is an excellent book on personalization and recommendations systems, from a prominent leader in the field. The book successfully serves multiple purposes: It is excellent as a reference, providing a comprehensive picture of the state of the art on recommendation systems, including not only the technical details, but also social-impact issues, like fairness, 'filter bubbles' ('echo chambers'),and the closely related topic of diversity. The second role is as a teaching resource: it has a gentle, intuitive coverage of all the necessary concepts and it provides exercises with solutions, as well as class projects. The third role is as a general, well-motivated introduction to almost all ML topics: supervised methods like regression and classification; unsupervised ones like matrix factorization; time series tools like Markov chains; text analysis; and deep learning. The final role is as a research tool: for practitioners and researchers, the book provides python code as well as a well-organized web site with about 30 datasets that researchers could use to stress-test their new algorithms.' Christos Faloutsos, Carnegie Mellon University '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 '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