  Recommender systems utilize users' historical data to learn and predict their
future interests, providing them with suggestions tailored to their tastes.
Calibration ensures that the distribution of recommended item categories is
consistent with the user's historical data. Mitigating miscalibration brings
various benefits to a recommender system. For example, it becomes less likely
that a system overlooks categories with less interaction on a user's profile by
only recommending popular categories. Despite the notable success, calibration
methods have several drawbacks, such as limiting the diversity of the
recommended items and not considering the calibration confidence. This work,
presents a set of properties that address various aspects of a desired
calibrated recommender system. Considering these properties, we propose a
confidence-aware optimization-based re-ranking algorithm to find the balance
between calibration, relevance, and item diversity, while simultaneously
accounting for calibration confidence based on user profile size. Our model
outperforms state-of-the-art methods in terms of various accuracy and
beyond-accuracy metrics for different user groups.
