Netflix has a secret weapon that keeps people coming back for more: its recommendation algorithm. The streaming service’s recommendations are accurate to the length that the algorithm recommends 75% of what people watch on Netflix.
Netflix takes into account a variety of factors when making recommendations. It starts by including users’ watching histories, their ratings of movies and TV shows, the genres they like, and what time of day they watch.
It also looks at how well a movie or TV show is doing with other Netflix users if you know how to get American Netflix in Australia. Hence, this algorithm is constantly being tweaked. And it seems to get better at guessing what you’ll want to watch next.
But have you ever thought about how the Netflix movie recommendation algorithm works? In this blog post, we will learn how Netflix recommends new movies to watch. And explore some of the algorithms and techniques used to make those recommendations.
How Does it Work?
Netflix has established itself as one of the most popular streaming services in the world. The company has over 100 million users. Its algorithm plays a major role in determining what content is recommended to users.
The algorithm is a system that considers various factors to create personalized recommendations for each user. It also evaluates user behavior and covers different data points. Such as ratings, watches history, and demographics.
Netflix’s “personalization” model considers each user’s viewing history and preferences. The more a user watches Netflix, the more the algorithm adapts to their tastes. Later, it ends with serving up tailored recommendations for further watching.
Typically, there is a 90-second window when Netflix captures a viewer’s attention. To win this, they must promote videos with a high probability of being viewed. With that, Netflix’s algorithm uses collaborative filtering.
Here, it considers the ratings of all users to make suggestions. So, for example, if you rate the movie “The Shawshank Redemption” as five stars, Netflix will recommend other movies that you may also like.
Does Netflix Know Everything About Our Likes?
Netflix has over 100 million users. And it would be impossible for every one of them to watch the same content. So, how does Netflix recommend movies and TV shows that a particular subscriber might want to watch?
To figure out what people want to watch next, the company built one of the world’s most complex movie recommendation algorithms. The algorithm takes into account over 100 factors, including:
- What you’ve watched in the past
- Type of genre you prefer
- What time in the day do you prefer to watch
- What country you’re from
- Netflix also tracks what you do on its website and app.
This means that it looks at the ratings that users have given to movies. But it also compares them with the ratings that other users have given to the same movies. It then analyzes patterns in the data and likes of the users to find similarities between them.
Personalization Finding Its Way into Netflix’s Algorithms
Netflix has something of a love-hate relationship with its movie recommendation algorithm. It’s always trying to find the right balance between personalization and pleasing most of its users.
Unfortunately, too much personalization can lead to too obscure or niche suggestions for the average viewer.
But not enough personalization can lead to a movie recommendation algorithm similar to everyone else’s. Hence, Netflix’s approach to personalization is constantly evolving. Netflix has always been a trailblazer in the movie recommendation space.
It starts from its humble beginnings of just providing a list of 10 titles based on what you’ve already watched. Now, it evolved into a much more sophisticated system that considers 700+ different factors to personalize your recommendations.
But contrarily, the level of personalization it offers the customers is unprecedented in the industry. Yet, Netflix’s competitors are starting to catch up with the new trend.
Amazon and Hulu both offer personalization features similar to Netflix’s. However, their algorithms are not as refined as Netflix’s. This is largely due to Netflix’s amount of data about its customers. Netflix has been collecting user data since its inception in 1997.
New Features Supporting Netflix’s Recommendation Algorithms
Netflix has brought personalization to a whole new level with its movie recommendation algorithms. The streaming service takes into account dozens of factors when it creates its custom lists for each individual. Mostly, ranging from the genres they’ve watched before to the actors they like.
In 2017, the company debuted the “My List” feature that allows users to create custom playlists. This level of personalization is even more customized than the recommendations. This is because Netflix depends on users’ viewing habits.
But as Netflix found, there are always new levels of personalization to be explored. The first thing Netflix does is ask you to rate a few titles after you finish watching them. It gives the algorithm a good idea of what you liked and didn’t like.
Then it looks at all of the other members who watched the same things as you and found other users with similar tastes. Later, with Netflix’s rating system, it assigns a score from 1 to 5 stars to movies and TV shows. It allows Netflix to track how much people like or dislike certain content.
Netflix has long been known for its personalized recommendations. The company’s recommendation algorithms are constantly tuned and tweaked to ensure that every user sees a different set of recommendations tailored specifically for them.
Netflix has always been tight-lipped about how its algorithms work, but a few years ago, it decided to open up its code to the public. It allowed developers everywhere to start playing around with the data and see what they could come up with.