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.
Why It Matters
Collaborative filtering powers most major recommendation engines. It can discover non-obvious connections between users and items that content analysis would miss.
Example
If users who liked movies A, B, and C also tend to like movie D, and you liked A, B, and C, the system recommends movie D — even without analyzing the movies' content.
Think of it like...
Like getting restaurant recommendations from friends with similar taste — 'If you liked the Thai place, you'll love this new Vietnamese spot.'
Related Terms
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.
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.
Cold Start Problem
The challenge of making recommendations for new users (who have no history) or new items (which have no ratings). Cold start is a fundamental difficulty in recommendation systems.