What are contextual bandits and where are they applied?

Contextual bandits are a type of reinforcement learning algorithms where the agent chooses an action (e.g., a recommendation) based on the current context (e.g., user information) and receives feedback only on the chosen action. Unlike the classic multi-armed bandit problem, where there is no context, here additional information is considered, allowing for more personalized decisions.

Applications:

• Recommendation systems (e.g., showing users relevant products or articles)
• Ad personalization
• Interface optimization and A/B testing
• Medical decisions, where treatment is chosen based on patient characteristics

Example: when displaying a news feed, the algorithm selects an article considering the user's profile and improves future choices based on clicks.