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.
Why It Matters
Cold start affects every recommendation system's launch phase and every new user's first experience. Solutions determine whether users stick around or leave.
Example
A new user signing up for Spotify with no listening history — the system has no data to personalize recommendations, so it must use other signals like demographics.
Think of it like...
Like being a new student at a school — nobody knows you yet, so the cafeteria cannot recommend meals based on your preferences until you have been there a while.
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.
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.