Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.01503 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918478148009984 |
|---|---|
| author | De Pasquale, Giulia Dean, Sarah Frasca, Paolo |
| author_facet | De Pasquale, Giulia Dean, Sarah Frasca, Paolo |
| contents | We propose a control-theoretic interpretation of recommender systems and use this perspective to analyze how fairness interventions shape long-term system behavior. Fairness concerns arise for both users and creators, ranging from opinion polarization and representation bias on the user side to popularity bias on the creator side. A central insight of our analysis is that fairness should not be viewed as a simple trade-off against utility. When optimized over time, it can in fact be beneficial for overall system performance. Realizing these gains, however, requires a clear understanding of the underlying dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01503 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Recommender Systems as Control Systems De Pasquale, Giulia Dean, Sarah Frasca, Paolo Systems and Control We propose a control-theoretic interpretation of recommender systems and use this perspective to analyze how fairness interventions shape long-term system behavior. Fairness concerns arise for both users and creators, ranging from opinion polarization and representation bias on the user side to popularity bias on the creator side. A central insight of our analysis is that fairness should not be viewed as a simple trade-off against utility. When optimized over time, it can in fact be beneficial for overall system performance. Realizing these gains, however, requires a clear understanding of the underlying dynamics. |
| title | Recommender Systems as Control Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2605.01503 |