How Your Favorite Streaming Services Knows What To Recommend You

Recommendations have gotten pretty accurate. From advertisements for products you've been looking at online to movies and show recommendations on your favorite streaming service — technology just seems to know what matches your interests. That's especially true of platforms like Netflix, which is built on personalized recommendations to encourage its subscription model. You keep finding shows, movies, and games you like, which hopefully translates to renewing your subscription. But how do streaming services like Netflix always seem to know exactly what to recommend? It all revolves around the Netflix algorithm.

When you first create your Netflix account, and there's no activity, the service asks you to choose a few titles of interest. Your initial recommendations are built using that information as a "jump start." From there, the recommendations shift based on how you engage and watch across the Netflix platform. Data on viewing history and title ratings factor in, but also the time of day you're browsing, languages you prefer, devices you use, how long you watched certain titles or content — like whether you finish a show — and even interactions of other members with similar tastes all influence your recommendations. So even though you might agree that Netflix original movies aren't very good, you may still see these titles as recommendations because of related content you've watched.

As mentioned, the real star of the show here is the Netflix algorithm processing all that data. It helps the system recommend movies and shows accurately, and gets better over time. As more people use Netflix, it can provide more relevant recommendations. 

A basic computer algorithm is a series of steps or instructions meant to achieve a certain result. But in Netflix's case, the algorithm is more complex as it factors in robust forms of data like user engagement and personal interests. It's difficult to quantify data output when the input is not mathematical, but AI and machine learning solutions don't reason this way. There is a difference between how humans and AI 'think'. Artificial intelligence simply follows instructions, and as more data is fed into the system, it becomes more proficient. One aspect of the Netflix algorithm might reason that if a user likes one movie, then they'll probably like other similar movies. Layering of multiple rules, for the algorithm to solve several equations, is what delivers targeted outputs — like Netflix recommendations — that are truly spot on.

As Netflix describes, your platform usage data and that of similar users provides the signals, or inputs, for the recommendation algorithms. It's not perfect, but Netflix continually updates the algorithms with data to improve the prediction accuracy. That data feeds into the overall user experience to produce "fresh recommendations" and make the platform more interesting to its users. As this use of AI-based algorithms continues to advance, keep an eye on disruptor platforms like Showrunner that claim to be the "Netflix of AI."