# Calibrated Recommendations: Survey and Future Directions

arXiv: https://arxiv.org/abs/2507.02643 · HTML: https://mlsanity.com/paper/2507.02643/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2025.

## Built on by (most cited first, 3 of 3)

- [MCGrad: Multicalibration at Web Scale](https://mlsanity.com/paper/2509.19884/) (2025)
- [Understanding Distribution Structure on Calibrated Recommendation Systems](https://mlsanity.com/paper/2508.13568/) (2025)
- [Calibrated Preference Learning: The Case of Label Ranking](https://mlsanity.com/paper/2605.30447/) (2026)

## Abstract (arXiv)

The idea of calibrated recommendations is that the properties of the items that are suggested to users should match the distribution of their individual past preferences. Calibration techniques are therefore helpful to ensure that the recommendations provided to a user are not limited to a certain subset of the user's interests. Over the past few years, we have observed an increasing number of research works that use calibration for different purposes, including questions of diversity, biases, and fairness. In this work, we provide a survey on the recent developments in the area of calibrated recommendations. We both review existing technical approaches for calibration and provide an overview on empirical and analytical studies on the effectiveness of calibration for different use cases. Furthermore, we discuss limitations and common challenges when implementing calibration in practice.