A Survey of Causal Inference Applications at Netflix
netflixtechblog.com
A Survey of Causal Inference Applications at Netflix
Netflix came to a similar conclusion for improving its recommendation algorithm. Decoding movies’ traits to figure out what you like was very complex and less accurate than simply analogizing you to many other customers with similar viewing histories. Instead of predicting what you might like, they examine who you are like, and the complexity is ca
... See moreDavid Epstein • Epstein_D_-_Range_Why_Generalists_Triumph_in_a_Specialized_World-Penguin_Publishing_Group_2019
employing analytical methods to predict treatment impact (i.e., medical influence) similar to the uplift modeling techniques used for marketing treatment decisions.
Eric Siegel • Predictive Analytics
The company challenged the world by requiring that the winner improve upon Netflix’s own established recommendation capabilities by 10 percent. Netflix is a prime example of PA in action, as a reported 70 percent of Netflix movie choices arise from its online recommendations.
Eric Siegel • Predictive Analytics
they enable us to test a causal model, discover new models, evaluate effects of interventions, and much more.
Judea Pearl, Dana Mackenzie • The Book of Why
they enable us to test a causal model, discover new models, evaluate effects of interventions, and much more.
Judea Pearl, Dana Mackenzie • The Book of Why
ost existing recommender systems fail to offer us recsplanations (i.e., human-understandable explanations for why they’ve recommended specific things). This makes it difficult for us to estimate how much satisfaction we might experience from a given option. Research in the field of recommender systems shows that recsplanations increase user satisfa... See more