Behind the Recommendation Engine | AI Learning Guide

So, how did the simulation work? We used Collaborative Filtering. It relies on the idea that users who agreed in the past will agree in the future. If Charlie likes what Alice likes, we recommend what Alice liked but Charlie hasn't seen yet. There is also Content-Based Filtering. Instead of looking at other users, it looks at the item itself. If you watch a lot of action movies, the system recommends more action movies because of the genre tags, regardless of what anyone else thinks.

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Collaborative Filtering

This method predicts a user's interests by collecting preferences from many users. It assumes that if User A and User B agreed on something in the past, they are likely to agree again in the future. This is how Spotify suggests songs based on what similar listeners enjoy.

Content-Based Filtering

This method recommends items similar to those a user liked in the past, based on item attributes. If you watch many sci-fi movies, the system will recommend other sci-fi movies because of the genre tags, regardless of what other users think.