Calibrated recommendations match user's actual preference distribution — if a user likes 70% action movies and 30% comedies, recommendations should reflect that ratio, ensuring recommendations align with user's true taste profile rather than over-optimizing for single preferences.

What Is Calibration?

• Definition: Recommendations match user's preference distribution.
• Example: User likes 60% rock, 30% jazz, 10% classical → recommendations should reflect this ratio.
• Goal: Balanced recommendations reflecting full taste profile.

Why Calibration Matters?

• User Satisfaction: Users want variety matching their tastes.
• Avoid Over-Specialization: Don't only recommend user's #1 preference.
• Fairness: Give all user interests appropriate attention.
• Discovery: Maintain exposure to all user interests.
• Long-Term: Prevent narrowing of user interests over time.

Calibration vs. Accuracy

Accuracy: Predict what user will like (may focus on dominant preference). Calibration: Match distribution of user's preferences (balanced across interests). Trade-off: Most accurate items may not be calibrated.

Measuring Calibration

KL Divergence: Distance between user preference distribution and recommendation distribution. Distribution Matching: Compare histograms of user preferences vs. recommendations. Category Coverage: Ensure all user interest categories represented.

Calibration Techniques

Re-Ranking: Adjust recommendation order to match preference distribution. Sampling: Sample recommendations from user's preference distribution. Constraint Optimization: Optimize accuracy subject to calibration constraints. Multi-Objective: Balance accuracy and calibration objectives.

Applications: Music recommendations (genre diversity), news (topic diversity), e-commerce (product category diversity), video streaming.

Challenges: Estimating user preference distribution, balancing calibration with accuracy, handling evolving preferences.

Tools: Calibrated recommendation algorithms, distribution matching methods.

Calibrated recommendations provide balanced, satisfying experiences — by matching user's full taste profile rather than over-optimizing for dominant preferences, calibration ensures recommendations feel right and maintain user interest diversity.