“Find the 10 most similar items to this one” sounds simple. With millions of items represented as 256-dimensional vectors, exact search is too slow to be useful in production. What Embeddings Are An ML model maps an item (a product, a document, a user’s history) to a dense numeric vector. The geometry of that vector space encodes semantic similarity: similar items land close together. You train the model on interaction data and the embeddings learn to represent “things that users treat similarly.

You don’t know what a user wants. But you know what people like them have wanted. That’s the intuition behind collaborative filtering. The Two Approaches User-based CF finds users similar to you, then recommends what they liked. Item-based CF finds items similar to what you’ve already liked. Item-based is generally more stable because user behavior shifts rapidly (you might buy a couch once), while item similarity changes slowly (a couch is similar to other furniture regardless of who buys it).