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Recommendations

How recommendation systems work, why they fail in real life, and what it takes to get suggestions that fit your mood and taste.

Want fast shortlist-style recommendations? Explore movie vibes.

Stylized film strip showing various musical movie posters
Feb 2, 2026·5 min read·Austin Burke

Musicals for people who think they hate musicals

If bursting into song makes you cringe, you might just be watching the wrong musicals. Here are the films that convert skeptics.

Person wrapped in blanket on couch with tea, soft TV glow, tissues nearby
Dec 29, 2025·5 min read·Austin Burke

Sick day movies: what to watch when you're unwell

Being sick changes what you want from a movie. Certain films work when you're under the weather for specific reasons, and picking the right one matters more than usual.

Illustration of a very long streaming watchlist with a scroll indicator showing hundreds of items
Dec 1, 2025·6 min read·Austin Burke

Why your Netflix queue never gets shorter (and what to do about it)

Your watchlist has 200 titles and keeps growing. Here's the psychology behind infinite queues and practical ways to actually watch what you save.

Person overwhelmed scrolling through streaming service grid of thumbnails
Nov 17, 2025·3 min read·Austin Burke

Stop scrolling, start watching: a better way to choose what to watch

Decision fatigue is real. Here's a practical framework (and a tool) for picking something you'll actually enjoy.

Empty user profile silhouette with question marks around it, streaming tiles in background
Nov 10, 2025·6 min read·Austin Burke

The cold start problem: how recommendation systems handle new users

When you sign up for a streaming service, it knows nothing about you. Here's how recommendation algorithms try to figure out your taste from scratch.

Netflix-style homepage showing "Recommended For You" section with mismatched content
Nov 3, 2025·6 min read·Austin Burke

Beyond the algorithm: why streaming recommendations miss the mark

Netflix knows a lot about what you've watched, but recommendations can still feel off. Here's why platform algorithms miss the mark and what actually helps.