In search of the perfect movie recommendation

It’s one of the most common low-stakes annoyances in modern life: you flop down on the couch at the end of the day, finally with a few minutes to watch one of the dozens of incredible shows or movies you have access to thanks to the peak TV era and the advent of streaming, and you start scrolling. Instead of actually watching anything, you spend an interminable evening opening apps, aimlessly scrolling through endless rows of same-looking tiles. You eventually give up and watch The Office again.

On this episode of The Vergecast, we look at why TV and movie recommendations are so complicated, and whether AI might be able to make them better. If Spotify can build infinite playlists of music you’ll like, and YouTube and TikTok always seem to have the perfect thing ready to go, why can’t Netflix or Hulu or Max seem to get it right?

AI, it turns out, can help at least a little. Because models from OpenAI, Google, and others have ingested so much information about movies and shows — not just their title and genre, but all the synopses, reviews, recaps, and more from all over the web — they can synthesize that information and find connections between titles that were previously hard to find. And as context windows get larger, these models can actually ingest and understand an entire film at once, which opens up entirely new ways of understanding them.

Ultimately, though, recommendations are a human problem. Because we’re all human. What you want to watch, and why you like what you like, are far more complicated — and vary far more widely — than even the best model can understand. As a result, the idea of sitting down, opening Netflix, and having the exact right title appear immediately, isn’t coming true anytime soon. So instead of hoping for the best, we investigate the ways to use AI tools right now to get to your content at least a little faster. Because watching movies great; scrolling through too many of them is seriously overrated.

If you want to know more about everything we discuss in this episode, here are a few links to get you started: