A system uses user and item data to predict what people may like.
A recommender system is like a snack-pushing friend. You eat one chip, and it slides over the whole bag.
You meet it in online shops. Video apps and news feeds use it to help you search less and stay longer.
Collaborative Filtering
Recommender systems use it to find clues from similar users.
Matrix Factorization
It splits the user-item table and guesses unknown likes.
Embedding
Embedding turns interests and items into vectors for easy comparison.
Bandit
Online recommenders use it to choose between safe picks and new tries.