Yunqi Li

Personalized Counterfactual Fairness in Recommendation

2021 · arXiv (Cornell University)

Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential unfairness …

Towards Personalized Fairness based on Causal Notion

2021

Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential unfairness …

Towards Long-term Fairness in Recommendation

2021

As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior approaches to fairness-aware recommendation have been …

User-oriented Fairness in Recommendation

2021

As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects the system performance. Therefore, it is important …