Recommendation System
An AI system that predicts and suggests items a user might be interested in based on their behavior, preferences, and similarities to other users.
Why It Matters
Recommendation systems drive 35% of Amazon's revenue and 80% of Netflix views. They are among the most commercially valuable AI applications.
Example
Netflix suggesting shows based on your viewing history, ratings, and what similar users enjoyed — personalizing content discovery for each of its 200+ million subscribers.
Think of it like...
Like a friend who knows your taste perfectly — they can walk into a bookstore and pick out exactly the books you would love, without you having to browse.
Related Terms
Collaborative Filtering
A recommendation technique that predicts a user's interests based on the preferences of similar users. It assumes people who agreed in the past will agree again in the future.
Content-Based Filtering
A recommendation technique that suggests items similar to those a user has previously liked, based on the items' features and attributes rather than other users' behavior.
Embedding
A numerical representation of data (text, images, etc.) as a vector of numbers in a high-dimensional space. Similar items are placed closer together in this space, enabling machines to understand semantic relationships.