An Amateur Attempt at Explaining Netflix Algorithms

With over 167 million users across 130 different countries and 61.04 million(Stasia) of those being from the United States alone, Netflix has evolved into the global leader in video streaming. Netflix has become an icon for a generation that no longer needs to go through the pain of going to the red box or blockbuster to rent a film. The streaming services offer us millions of TV shows, movies, and documentaries all at our fingertips and with over 76,897 micro-genres (Tech-Times) the options are endless for users.

While there are a plethora of new streaming services popping up such as Disney-Plus, Crunchyroll Premium, and of course Hulu, Netflix has been able to maintain its dominance over the industry for a few reasons. One of the more obvious reasons being that Netflix has an absurd amount of original content original only to its platform. According to showbiz cheatsheet, Netflix released nearly 400 original shows and movies in 2019. Not to mention they already have established originals such as the popular show Stranger Things and Narcos. However, their ability to pump money into producing original content for their platform is not the unique reason for their dominance. They do one thing that other streaming companies cannot seem to replicate: get you hooked.

We have all been through it, we sit down to just watch “one episode” of a series and end up binging the whole thing in two weeks and then begin asking questions of our mortal existence the moment we finish. Is it your fault that you forgot about your family to focus on the 4 seasons of Stranger Things? Perhaps, but Netflix has some subtle yet effective ways in which they get you to keep on watching more, and more, and more.

Netflix has developed the perfect user interface to keep you glued to the screen for hours. They have a recommendation system which allows users to see the percentage of likelihood that they will enjoy it. In addition to their recommendations, they have adapted their platform to give the user the option to skip the introduction of certain shows, and also skip the credits at the end of a show or a movie. If you are a nerd like me and wonder while you are watching food documentaries: “how do they do that?!”, this is the right place for you. In this article, we will be going over the ins and outs of Netflix’s recommendation system, and how they are able to allow intro and credit skipping in certain shows, and why all these things add up to create the world’s largest video streaming platform.

Skipping the Intro

At times an introduction song to a show can be the greatest thing in the world. However, 7 hours into your binge, after the episode that just left off on a huge cliff hanger , and before the season finale, you just want to get to the good stuff. Netflix has noticed this and have presented users with an option to skip intros at the beginning of their show. Let’s take a closer look at how and why they do this.

Challenges with Skipping the Intro:

Essentially, Netflix had to develop an algorithm which identifies the start of every intro in every episode of every show, and the end time of these intros. The largest problem they have is: not all intros begin and end at the same time. Some shows play a scene before their introduction, and those scenes differ in length all the time. Also, introductions are constantly changing, some intros depending on the show ( I am mainly thinking about anime) , so they have to adapt the algorithm for that. The point is: how does Netflix’s dynamic intro skipping work? Humans? Machine Learning? Audio Recognition? Here are some of the ways I found that could make this possible. Let me preface: no research has pointed me to the exact algorithm that they use, these are simply speculations based on likelihood.

Option 1: Humans

Imagine a poor soul going through each and every episode and every show and finding the start and end time of the introduction. Picture it in your head right now, some lonely person with their headphones on having to do this for dozens of shows for 8 hours a day. Sound crazy? It should. There is no way that someone is doing this.

Option 2: Machine Learning

Machine learning is a hot topic and a very scary word for those who do not quite understand what this means. From the perspective of a computer science student, simply put machine learning is the process in which an algorithm is fed data, and asked to predict a result. The algorithm is “trained” with different data until it “learns” to produce a good result.

Relating this back to Netflix, perhaps this is how they spot the intro’s of shows. Maybe they go through all their titles and run this algorithm to find the start and end time of a show’s introduction to then properly predict the output. Perhaps also Netflix is gathering data from their users who watch these titles to feed into the algorithm. For example, before this feature was available, one would most likely skip through introductions, every time that happened Netflix would get data which would then go into feeding the algorithm. And of course, with a plethora of current Netflix original titles, I am sure those titles need to provide the company with time stamps of the introduction before release.

Option 3: Audio Recognition

Because introductions are usually of a certain length, perhaps Netflix uses the length of the song of an introduction to skip the end length of it. The algorithm would begin as soon as their algorithm recognizes the song, and then the skip intro button would show up allowing the user to skip as soon as the audio is recognized.

Perhaps Netflix has a library of songs, and if the introduction matches the library of songs then the button shows up.

Recommendation System:

We will work toward understanding just how Netflix recommends you what they do. Netflix uses data which it collects from you to feed into machine learning algorithms so it can display options you might like. The basis of how it works is Netflix reads in a bunch of data it gets from you watching, trains an algorithm to recommend things to you based on certain criteria, and then it displays it in a way that you are most likely to watch.

Some of the criteria that Netflix gathers according to their website are:

Netflix has made it clear that they do not use demographic information to feed into the algorithm. However, it is clear to them based on these what type of shows you might like to watch, and what they should recommend to you. Their enhanced way of showing you what you may like allows them to maintain a stronger user count. Another aspect of this is their user-interface design. According to the Netflix website, they rank each title within a row of recommendations ( which can represent a certain genre) and then rank the rows themselves based on your watching habits. For example, if you watched a show quite a bit, eventually you may see a row of recommendations labeled “More shows like…” which will provide you a list of shows similar to the one you watch the most, ranked in order of similarity. However, Netflix does not only use your data to calculate this, but data from others as well. These rows are based off of data from millions of users. For example, their algorithm tracks how many users that watched one show watched this other show and if the number is high than the percentage match is higher to you. While Netflix’s algorithm seems very personal and customized to the user, it is actually training the algorithm based off of millions of users, including your own.

Here are some ways in Netflix claims that they order their rows:

Conclusion:

With the coronavirus forcing many to work from home, we have a lot of free time on our hands.It is a safe bet to say that in the next few weeks in the United States will be a crucial one for video streaming companies due to the influx of traffic caused by the virus. With that said, perhaps this article has helped you reach a clearer understanding of how Netflix keeps you glued to the screen.

Netflix uses a combination of their wonderful Machine Learning algorithms and their sleek and intuitive user interface design to maintain a large user base. So perhaps the next time you sit down and binge a show on Netflix, you do not have to seem so guilty because they are trying to get you to watch more, and more, and more. Going in with a elementary level understanding of how Netflix crafts their software is the first step into taking a hold of why and what exactly people are watching, and the first step into understanding the large roles machine learning plays in our lives today.

Works Cited:

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