\BOOKMARK [0][-]{chapter*.2}{List of illustrations}{}% 1
\BOOKMARK [0][-]{chapter*.3}{List of tables}{}% 2
\BOOKMARK [0][-]{chapter*.4}{Notation}{}% 3
\BOOKMARK [0][-]{chapter.1}{Introduction}{}% 4
\BOOKMARK [1][-]{section.1.1}{Purpose of This Book}{chapter.1}% 5
\BOOKMARK [1][-]{section.1.2}{For Learners: What is Covered, and What Isn't}{chapter.1}% 6
\BOOKMARK [1][-]{section.1.3}{For Instructors: Course and Content Outline}{chapter.1}% 7
\BOOKMARK [1][-]{section.1.4}{Online Resources}{chapter.1}% 8
\BOOKMARK [1][-]{section.1.5}{About the Author}{chapter.1}% 9
\BOOKMARK [1][-]{section.1.6}{Personalization in Everyday Life}{chapter.1}% 10
\BOOKMARK [1][-]{section.1.7}{Techniques for Personalization}{chapter.1}% 11
\BOOKMARK [1][-]{section.1.8}{The Ethics and Consequences of Personalization}{chapter.1}% 12
\BOOKMARK [-1][-]{part.1}{}{}% 13
\BOOKMARK [0][-]{chapter.2}{Regression and Feature Engineering}{part.1}% 14
\BOOKMARK [1][-]{section.2.1}{Linear Regression}{chapter.2}% 15
\BOOKMARK [1][-]{section.2.2}{Evaluating Regression Models}{chapter.2}% 16
\BOOKMARK [1][-]{section.2.3}{Feature Engineering}{chapter.2}% 17
\BOOKMARK [1][-]{section.2.4}{Interpreting the Parameters of Linear Models}{chapter.2}% 18
\BOOKMARK [1][-]{section.2.5}{Fitting Models with Gradient Descent}{chapter.2}% 19
\BOOKMARK [1][-]{section.2.6}{Non-linear Regression}{chapter.2}% 20
\BOOKMARK [1][-]{section*.24}{Exercises}{chapter.2}% 21
\BOOKMARK [1][-]{section*.25}{Project 1: Taxicab Tip Prediction \(Part 1\)}{chapter.2}% 22
\BOOKMARK [0][-]{chapter.3}{Classification and the Learning Pipeline}{part.1}% 23
\BOOKMARK [1][-]{section.3.1}{Logistic Regression}{chapter.3}% 24
\BOOKMARK [1][-]{section.3.2}{Other Classification Techniques}{chapter.3}% 25
\BOOKMARK [1][-]{section.3.3}{Evaluating Classification Models}{chapter.3}% 26
\BOOKMARK [1][-]{section.3.4}{The Learning Pipeline}{chapter.3}% 27
\BOOKMARK [1][-]{section.3.5}{Implementing the Learning Pipeline}{chapter.3}% 28
\BOOKMARK [1][-]{section*.39}{Exercises}{chapter.3}% 29
\BOOKMARK [1][-]{section*.40}{Project 2: Taxicab Tip Prediction \(Part 2\)}{chapter.3}% 30
\BOOKMARK [-1][-]{part.2}{}{}% 31
\BOOKMARK [0][-]{chapter.4}{Introduction to Recommender Systems}{part.2}% 32
\BOOKMARK [1][-]{section.4.1}{Basic Setup and Problem Definition}{chapter.4}% 33
\BOOKMARK [1][-]{section.4.2}{Representations for Interaction Data}{chapter.4}% 34
\BOOKMARK [1][-]{section.4.3}{Memory-based Approaches to Recommendation}{chapter.4}% 35
\BOOKMARK [1][-]{section.4.4}{Random Walk Methods}{chapter.4}% 36
\BOOKMARK [1][-]{section.4.5}{Case Study: Amazon.com Recommendations}{chapter.4}% 37
\BOOKMARK [1][-]{section*.46}{Exercises}{chapter.4}% 38
\BOOKMARK [1][-]{section*.47}{Project 3: A Recommender System for Books \(Part 1\)}{chapter.4}% 39
\BOOKMARK [0][-]{chapter.5}{Model-based Approaches to Recommendation}{part.2}% 40
\BOOKMARK [1][-]{section.5.1}{Matrix Factorization}{chapter.5}% 41
\BOOKMARK [1][-]{section.5.2}{Implicit Feedback and Ranking Models}{chapter.5}% 42
\BOOKMARK [1][-]{section.5.3}{`User-free' Model-based Approaches}{chapter.5}% 43
\BOOKMARK [1][-]{section.5.4}{Evaluating Recommender Systems}{chapter.5}% 44
\BOOKMARK [1][-]{section.5.5}{Deep Learning for Recommendation}{chapter.5}% 45
\BOOKMARK [1][-]{section.5.6}{Retrieval}{chapter.5}% 46
\BOOKMARK [1][-]{section.5.7}{Online Updates}{chapter.5}% 47
\BOOKMARK [1][-]{section.5.8}{Recommender Systems in Python with Surprise and Implicit}{chapter.5}% 48
\BOOKMARK [1][-]{section.5.9}{Beyond a `Black-Box' View of Recommendation}{chapter.5}% 49
\BOOKMARK [1][-]{section.5.10}{History and Emerging Directions}{chapter.5}% 50
\BOOKMARK [1][-]{section*.61}{Exercises}{chapter.5}% 51
\BOOKMARK [1][-]{section*.62}{Project 4: A Recommender System for Books \(Part 2\)}{chapter.5}% 52
\BOOKMARK [0][-]{chapter.6}{Content and Structure in Recommender Systems}{part.2}% 53
\BOOKMARK [1][-]{section.6.1}{The Factorization Machine}{chapter.6}% 54
\BOOKMARK [1][-]{section.6.2}{Cold-Start Recommendation}{chapter.6}% 55
\BOOKMARK [1][-]{section.6.3}{Multisided Recommendation}{chapter.6}% 56
\BOOKMARK [1][-]{section.6.4}{Group- and Socially-Aware Recommendation}{chapter.6}% 57
\BOOKMARK [1][-]{section.6.5}{Price Dynamics in Recommender Systems}{chapter.6}% 58
\BOOKMARK [1][-]{section.6.6}{Other Contextual Features in Recommendation}{chapter.6}% 59
\BOOKMARK [1][-]{section.6.7}{Online Advertising}{chapter.6}% 60
\BOOKMARK [1][-]{section*.64}{Exercises}{chapter.6}% 61
\BOOKMARK [1][-]{section*.65}{Project 5: Cold-Start Recommendation on Amazon}{chapter.6}% 62
\BOOKMARK [0][-]{chapter.7}{Temporal and Sequential Models}{part.2}% 63
\BOOKMARK [1][-]{section.7.1}{Introduction to Regression with Time Series}{chapter.7}% 64
\BOOKMARK [1][-]{section.7.2}{Temporal Dynamics in Recommender Systems}{chapter.7}% 65
\BOOKMARK [1][-]{section.7.3}{Other Approaches to Temporal Dynamics}{chapter.7}% 66
\BOOKMARK [1][-]{section.7.4}{Personalized Markov Chains}{chapter.7}% 67
\BOOKMARK [1][-]{section.7.5}{Case Studies: Markov-Chain Models for Recommendation}{chapter.7}% 68
\BOOKMARK [1][-]{section.7.6}{Recurrent Networks}{chapter.7}% 69
\BOOKMARK [1][-]{section.7.7}{Neural Network-Based Sequential Recommenders}{chapter.7}% 70
\BOOKMARK [1][-]{section.7.8}{Case Study: Personalized Heart-Rate Modeling}{chapter.7}% 71
\BOOKMARK [1][-]{section.7.9}{History of Personalized Temporal Models}{chapter.7}% 72
\BOOKMARK [1][-]{section*.74}{Exercises}{chapter.7}% 73
\BOOKMARK [1][-]{section*.75}{Project 6: Temporal and Sequential Dynamics in Business Reviews}{chapter.7}% 74
\BOOKMARK [-1][-]{part.3}{}{}% 75
\BOOKMARK [0][-]{chapter.8}{Personalized Models of Text}{part.3}% 76
\BOOKMARK [1][-]{section.8.1}{Basics of Text Modeling: The Bag-of-Words Model}{chapter.8}% 77
\BOOKMARK [1][-]{section.8.2}{Distributed Word and Item Representations}{chapter.8}% 78
\BOOKMARK [1][-]{section.8.3}{Personalized Sentiment and Recommendation}{chapter.8}% 79
\BOOKMARK [1][-]{section.8.4}{Personalized Text Generation}{chapter.8}% 80
\BOOKMARK [1][-]{section.8.5}{Case Study: Google's Smart Reply}{chapter.8}% 81
\BOOKMARK [1][-]{section*.91}{Exercises}{chapter.8}% 82
\BOOKMARK [1][-]{section*.92}{Project 7: Personalized Document Retrieval}{chapter.8}% 83
\BOOKMARK [0][-]{chapter.9}{Personalized Models of Visual Data}{part.3}% 84
\BOOKMARK [1][-]{section.9.1}{Personalized Image Search and Retrieval}{chapter.9}% 85
\BOOKMARK [1][-]{section.9.2}{Visually-Aware Recommendation and Personalized Ranking}{chapter.9}% 86
\BOOKMARK [1][-]{section.9.3}{Case Studies: Visual and Fashion Compatibility}{chapter.9}% 87
\BOOKMARK [1][-]{section.9.4}{Personalized Generative Models of Images}{chapter.9}% 88
\BOOKMARK [1][-]{section*.99}{Exercises}{chapter.9}% 89
\BOOKMARK [1][-]{section*.100}{Project 8: Generating Compatible Outfits}{chapter.9}% 90
\BOOKMARK [0][-]{chapter.10}{The Consequences of Personalized Machine Learning}{part.3}% 91
\BOOKMARK [1][-]{section.10.1}{Measuring Diversity}{chapter.10}% 92
\BOOKMARK [1][-]{section.10.2}{Filter Bubbles, Diversity, and Extremification}{chapter.10}% 93
\BOOKMARK [1][-]{section.10.3}{Diversification Techniques}{chapter.10}% 94
\BOOKMARK [1][-]{section.10.4}{Implementing a Diverse Recommender}{chapter.10}% 95
\BOOKMARK [1][-]{section.10.5}{Case Studies on Recommendation and Consumption Diversity}{chapter.10}% 96
\BOOKMARK [1][-]{section.10.6}{Other Metrics Beyond Accuracy}{chapter.10}% 97
\BOOKMARK [1][-]{section.10.7}{Fairness}{chapter.10}% 98
\BOOKMARK [1][-]{section.10.8}{Case Studies on Gender Bias in Recommendation}{chapter.10}% 99
\BOOKMARK [1][-]{section*.107}{Exercises}{chapter.10}% 100
\BOOKMARK [1][-]{section*.108}{Project 9: Diverse and Fair Recommendations}{chapter.10}% 101