More than 80 per cent of the TV shows people watch on Netflix are discovered through the platform’s recommendation system.

  1. Perform a scenario design analysis as described below. Consider whether it makes sense for your selected recommender system to perform scenario design twice, once for the organization and once for the organization’s customers.

Netflix is the world’s leading Internet television network with over 125 million members in over 190 countries enjoying more than 140 million hours of TV shows and movies per day, including original series, documentaries and feature films. Members can watch as much as they want, anytime, anywhere, on nearly any Internet-connected screen. Members can play, pause and resume watching, all without commercials or commitments. The Netflix business model uses data science heavily to create new shows, add new movies and episodes and even new categories. It receives viewers show rating as an input and uses it to build recommendation system and hence creating more personalized experience for its viewers. It has also started producing its own shows and getting nominated to Oscars as well almost every year.

Netflix does run A/B testing on it’s site and it learns from both biz users and subscribers continuosly.

  1. Attempt to reverse engineer what you can about the site, from the site interface and any available information that you can find on the Internet or elsewhere.

Data science is the backbone of Netflix and helping it to create new business opportunities and launch new data services as well. It has definitely changed the way we used to watch TV shows and movies.

Grand Prize - RMSE = 0.8567 - Winning Team: BellKor’s Pragmatic Chaos
“BellKor’s Pragmatic Chaos” for being awarded the $1M Grand Prize on September 21, 2009. RMSE test score is the main driving factor so far overall % improvement in the recommendation system.

  1. Include specific recommendations about how to improve the site’s recommendation capabilities going forward.

Netflix is continuously learning from the data which it collects from its viewers and uses that to optimize the business processes and of course recommender system. Public competetions such as Kaggle are most efficient ways to improve the overall systems as data scientists from all around the world participate.

References
https://www.kaggle.com/netflix-inc/netflix-prize-data
https://aws.amazon.com/solutions/case-studies/netflix/
https://www.netflixprize.com/community/topic_1537.html
https://www.netflixprize.com/leaderboard.html
https://www.netflixprize.com/rules.html