On the Theories Behind Hard Negative Sampling for Recommendation

7 February 2023

Papers citing "On the Theories Behind Hard Negative Sampling for Recommendation"

6 / 6 papers shown

Title
Debias Can be Unreliable: Mitigating Bias Issue in Evaluating Debiasing Recommendation Chengbing Wang Wentao Shi Jizhi Zhang Wenjie Wang Hang Pan Fuli Feng 123 1 0 07 Sep 2024
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee Dixian Zhu Gang Li Bokun Wang Xiaodong Wu Tianbao Yang 68 30 0 01 Mar 2022
DORO: Distributional and Outlier Robust Optimization Runtian Zhai Chen Dan J. Zico Kolter Pradeep Ravikumar 33 60 0 11 Jun 2021
Boosting the Speed of Entity Alignment 10*: Dual Attention Matching Network with Normalized Hard Sample Mining Xin Mao Wenting Wang Yuanbin Wu Man Lan 33 111 0 29 Mar 2021
Large-Scale Methods for Distributionally Robust Optimization Daniel Levy Y. Carmon John C. Duchi Aaron Sidford 56 212 0 12 Oct 2020
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering Jingtao Ding Yuhan Quan Quanming Yao Yong Li Depeng Jin 31 100 0 07 Sep 2020