Yingqiang Ge

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 …

CAFE

2020

Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to generate explanations of why particular decisions are made. This can be achieved by …

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 …

Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)

2022

For a long time, different recommendation tasks require designing task-specific architectures and training objectives. As a result, it is hard to transfer the knowledge and representations from one task to another, thus restricting the generalization …