Hanxiong Chen

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 …

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 …