Netflix Recommends

Algorithmic recommender systems, deployed by media companies to suggest content based on users’ viewing histories, have inspired hopes for personalized, curated media but also dire warnings of filter bubbles and media homogeneity. Curiously, both proponents and detractors assume that recommender systems for choosing films and series are novel, effective, and widely used. Scrutinizing the world’s most subscribed streaming service, Netflix, this book challenges that consensus. Investigating real-life users, marketing rhetoric, technical processes, business models, and historical antecedents, Mattias Frey demonstrates that these choice aids are neither as revolutionary nor as alarming as their celebrants and critics maintain—and neither as trusted nor as widely used. Netflix Recommends brings to light the constellations of sources that real viewers use to choose films and series in the digital age and argues that although some lament AI’s hostile takeover of humanistic cultures, the thirst for filters, curators, and critics is stronger than ever.

Acknowledgments 

Introduction
1 • Why We Need Film and Series Suggestions
2 • How Algorithmic Recommender Systems Work
3 • Developing Netflix's Recommendation Algorithms
4 • Unpacking Netflix's Myth of Big Data
5 • How Real People Choose Films and Series
Afterword: Robot Critics vs. Human Experts

Appendix. Designing the Empirical Audience Study
Notes
Selected Bibliography
Index