What is exploration vs exploitation in recommendation systems?
Updated May 16, 2026
Short answer
It balances recommending known good items vs exploring new items.
Deep explanation
Exploitation recommends items with known high relevance, while exploration tries new items to improve long-term learning. This tradeoff is often handled using multi-armed bandits or epsilon-greedy strategies.
Real-world example
Netflix occasionally recommending new genres to users.
Common mistakes
- Over-exploiting leading to filter bubbles.
Follow-up questions
- What is epsilon-greedy?
- What are bandit algorithms?