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.
Author(s): Julian McAuley
Publisher: Cambridge University Press
Year: 2022
Language: English
Pages: 350
City: Cambridge
00.0
01.0_pp_i_iv_Frontmatter
02.0_pp_v_viii_Contents
03.0_pp_ix_x_Preface
04.0_pp_1_16_Introduction
05.0_pp_17_18_Machine_Learning_Primer
05.1_pp_19_48_Regression_and_Feature_Engineering
05.2_pp_49_78_Classification_and_the_Learning_Pipeline
06.0_pp_79_80_Fundamentals_of_Personalized_Machine_Learning
06.1_pp_81_103_Introduction_to_Recommender_Systems
06.2_pp_104_143_Model-Based_Approaches_to_Recommendation
06.3_pp_144_176_Content_and_Structure_in_Recommender_Systems
06.4_pp_177_216_Temporal_and_Sequential_Models
07.0_pp_217_218_Emerging_Directions_in_Personalized_Machine_Learning
07.1_pp_219_251_Personalized_Models_of_Text
07.2_pp_252_272_Personalized_Models_of_Visual_Data
07.3_pp_273_305_The_Consequences_of_Personalized_Machine_Learning
08.0_pp_306_321_References
09.0_pp_322_326_Index
