De-blackbox the Algorithms of Netflix Recommendation

What makes me most interest in this week’s reading is “recommendation system” because it has been very commonplace in a variety of areas, such as Facebook news, Instagram, music App, etc. According to the recent survey conducted by Pew Research Center, most U.S. people confirmed that their social media could accurately define their key characteristics, such as hobbies and interests and etc. In fact, I am increasingly surprised by how my cell phone knows me so well by recommending me new videos and music that amazingly fit my taste.

According to the Wikipedia, recommendation systems typically produce a list of recommendations in one of two ways – through collaborative filtering or through content-based filtering. I would like to use Netflix as an example to explain how recommender systems works. Actually, Netflix combines the two kinds of recommendation system method. The website makes personal recommendations by comparing the watching and searching habits of similar users (i.e., collaborative filtering) as well as by offering movies that share characteristics with films that a user has rated highly (content-based filtering). (Wikipedia)

The video below gives more detailed explanation to how Netflix recommendation system works. In Netflix, a huge matrix factorization was created based on 2000 users’ previous rating and 1000 movies features with a kind of training model so that recommend every user’s fittest movie based on their movie preference. Many math’s calculation and error correction were continuously conducted in the process. To some extent, Netflix might know our movie preference better than ourselves.