CUGF: A Reliable and Fair Recommendation Framework
Nitin Bisht, Xiuwen Gong, Guandong Xu
Abstract
Recommendation systems (RS) play a crucial role in assisting decision-making but often suffer from either a lack of credibility or unfairness problems. A few recommendation models have endeavored to address the problem from only one aspect, and approaches to solving both problems remain to be explored. This paper aims to construct a generalized fairness-based recommendation framework that can also provide the credibility of recommendation models. Generally, we propose a reliable and fair recommendation framework called Conformalized User Group Fairness (CUGF) based on the inspiration of conformal prediction. Specifically, we construct dynamic prediction sets that are guaranteed to cover the true item with a user pre-specified probability to ensure credibility while designing novel fairness metrics based on empirical risks to guarantee the fairness of users across different groups. Furthermore, we design a novel CUGF Algorithm to optimize the parameter γ that dominates the prediction sets and also the fairness. Besides, we conduct extensive experiments by applying CUGF on top of various recommendation models and representative datasets to validate its effectiveness with respect to recommendation performance (in terms of average set size) and fairness (in terms of the two defined fairness metrics), the results of which demonstrate the validity of the proposed framework.
BibTeX
@article{Bisht_Gong_Xu_2025, title={CUGF: A Reliable and Fair Recommendation Framework}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/33245}, DOI={10.1609/aaai.v39i11.33245}, abstractNote={Recommendation systems (RS) play a crucial role in assisting decision-making but often suffer from either a lack of
credibility or unfairness problems. A few recommendation
models have endeavored to address the problem from only
one aspect, and approaches to solving both problems remain
to be explored. This paper aims to construct a generalized
fairness-based recommendation framework that can also provide the credibility of recommendation models. Generally,
we propose a reliable and fair recommendation framework
called Conformalized User Group Fairness (CUGF) based
on the inspiration of conformal prediction. Specifically, we
construct dynamic prediction sets that are guaranteed to cover
the true item with a user pre-specified probability to ensure
credibility while designing novel fairness metrics based on
empirical risks to guarantee the fairness of users across different groups. Furthermore, we design a novel CUGF Algorithm
to optimize the parameter γ that dominates the prediction sets
and also the fairness. Besides, we conduct extensive experiments by applying CUGF on top of various recommendation
models and representative datasets to validate its effectiveness with respect to recommendation performance (in terms
of average set size) and fairness (in terms of the two defined
fairness metrics), the results of which demonstrate the validity of the proposed framework.}, number={11}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Bisht, Nitin and Gong, Xiuwen and Xu, Guandong}, year={2025}, month={Apr.}, pages={11445-11453} }