27 citations · 28 across the 3 of their papers we have counts for
3 papers
cs.IR2023★ 1 cited
Ensuring User-side Fairness in Dynamic Recommender Systems
Hyunsik Yoo, Zhichen Zeng, Jian Kang +7
User-side group fairness is crucial for modern recommender systems, aiming to alleviate performance disparities among user groups defined by sensitive attributes like gender, race,…
cs.LG2020★ 27 cited
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
Zhining Liu, Pengfei Wei, Jing Jiang +3
Imbalanced learning (IL), i.e., learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed…
cs.LG2018
Scalable attribute-aware network embedding with locality
Weiyi Liu, Zhining Liu, Toyotaro Suzumura +1
Adding attributes for nodes to network embedding helps to improve the ability of the learned joint representation to depict features from topology and attributes simultaneously. Re…