Content-Based Filtering in Python — ELI5

Imagine a librarian who notices you always check out mystery novels with female detectives. Next time you visit, she points you to a new mystery with a female detective — not because other people liked it, but because she knows your pattern.

Content-based filtering works this way. Instead of looking at what other people enjoy, it studies the features of items you already liked and finds new items with similar features.

For movies, those features might be genre, director, actors, and plot keywords. For songs, tempo, mood, and instruments. For articles, the words and topics inside them.

Python programs turn these features into numbers, then use math to measure how “close” a new item is to things you’ve enjoyed before. The closer the numbers, the stronger the recommendation.

A common misunderstanding is that this approach always finds you something new and exciting. Actually, it tends to recommend things very similar to what you already know. If you only watch comedies, it will keep suggesting comedies — it won’t surprise you with a documentary you might love.

One thing to remember: content-based filtering reads the label on the box rather than asking the crowd — it recommends based on what an item is, not who else liked it.