Calibrated Recommendations: Survey and Future Directions
Abstract
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.
Fields of Study & Subjects
Recommender Systems and Techniques
Ethics and Social Impacts of AI
Mobile Crowdsensing and Crowdsourcing
Publication details
- Journal / source
- ACM Transactions on Recommender Systems
- Publisher
- Association for Computing Machinery (ACM)
- Volume
- 4
- Issue
- 3
- Pages
- 1–32
- Publication date
- 21 January 2026
- Language
- EN