According to ir.netflix.com, Netflix is (Spring 2018 data) the world's leading Internet television network with more than 118 million members in over 190 countries enjoying more than 150 million hours of TV shows and movies per day, including original series, documentaries, and feature films. Members can view unlimited shows without commercials for a monthly fee. Netflix has several million titles and now produces its own shows. The large titles inventory often creates a problem for customers who have difficulty determining which offerings they want to watch. An additional challenge is that Netflix expanded its business from the United States and Canada to 190 other countries.
Netflix operates in a very competitive environment in which large players such as Apple, Amazon.com, and Google operate. Netflix was looking for a way to distinguish itself from the competition by making useful recommendations to its customers.
The Original Recommendation Engine
Netflix originally was solely a mail-order business for DVDs. At that time, it encountered inventory problems due to its customers' difficulties in determining which DVDs to rent. The solution was to develop a recommendation engine (called Cinematch) that told subscribers which titles they probably would like.
Cinematch used data mining tools to sift through a database of billions of film ratings and customers' rental histories. Using proprietary algorithms, it recommended rentals to customers. The recommendation was accomplished by comparing an individual's likes, dislikes, and preferences against those of people with similar tastes, using a variant of collaborative filtering.
Cinematch was like the geeky clerk at a small movie store who sets aside titles he knows you will like and suggests them to you when you visit the store.
To improve Cinematch's accuracy, Netflix began a contest in October 2016, offering $1 million to the first person or team that will write a program that would increase Cinematch's prediction accuracy by at least 10 percent. The company understood that this would take quite some time; therefore, it offered a
$50,000 Progress Prize each year in which the contest was conducted. After more than two years of competition, the grand prize went to Bellkor's Pragmatic Chaos team, a combination of two runner-up teams.
To learn how the movie recommendation algorithms work, see quora.com/How-does-the-Netflixmovie recommendation-algorithm-work/.

Questions for Case 12.3
1. Why is the recommender system useful?
2. Explain how recommendations are generated.
3. Amazon disclosed its recommendation algorithms to the public but Netflix did not. Why?
4. Research the research activities that attempt to “mimic the human brain.”
5. Explain the changes due to the globalization of the company.