Netflix’s recommendation system is considered the best by many OTT viewers when compared to other players across the globe. But sometimes, you may have noticed it recommending strange titles based on your past searches. Right? At times, the streaming giant ends up suggesting a title from a completely different genre, simply because you watched something loosely connected in the past.
Many people are curious about how Netflix’s recommendation algorithm actually works, because sometimes, the streamer throws in completely random suggestions that seem to have no connection to your previous searches.
We came across a tweet (shared below) that highlights exactly this issue. The tweet shows Netflix recommending Metro… In Dino, “because he watched Karate Kid: Legends.” Strange and a bit funny, isn’t it?
Both movies couldn’t be more different. Forget the industries they belong to or the actors starring in them; even their genres are nowhere near the same. So why does something like this happen on Netflix?
The truth is, Netflix’s recommendation engine is far more sophisticated, and often far less intuitive to the human brain, than a simple genre-matching system. While we categorise films broadly, the algorithm operates at a much more granular level, uncovering connections that would otherwise remain invisible.
At its core, Netflix’s recommendation algorithm is built on collaborative filtering, the foundation of most modern recommendation systems. The principle is simple: if two people have shown similar tastes in the past, they’re likely to share similar tastes in the future.
Imagine Netflix’s comprehensive network of users. User A watches Karate Kid: Legends and enjoys it. User B also watches Karate Kid: Legends and enjoys it. Now, User B happens to watch Metro… In Dino and rates it highly. The algorithm spots this pattern: “People who liked ‘Karate Kid: Legends’ also tend to like ‘Metro… In Dino.’”
So even if you, as User A, have never shown interest in Bollywood or romantic dramas, the statistical correlations uncovered by thousands, or even millions, of other users’ viewing habits suggest that you might still enjoy Metro… In Dino.
And how is this connection established? Well, Netflix’s recommendation engine doesn’t just rely on shared genres; it focuses on shared viewing patterns.
Netflix even employs an army of “taggers” who assign extremely specific micro-genres and metadata to every piece of content. Not just prominent genres like “Action” or “Romance,” but things like “emotional coming-of-age story about a female protagonist” or “feel-good buddy comedy with strong female leads.”
Both of the titles mentioned above may share one or more of these less obvious tags.
In other words, Netflix’s recommendation engine is a calculated gamble based on population-level viewing data. The algorithm doesn’t understand movies the way a human does; it understands numerical relationships between viewing habits and content attributes.
So, our advice: don’t be too surprised if this happens again. It’s not a glitch, but rather an intriguing glimpse into the complex, data-driven world of AI recommendations, tirelessly working to connect you with your next favourite watch, even if some suggestions seem a bit strange. Stay tuned for more updates.