Countering Mainstream Bias via End-to-End Adaptive Local Learning: Debiasing Experiments and Setup | HackerNoon
Briefly

from Hackernoon9 months ago
The proposed method aims to enhance debiasing effectiveness by optimizing for NDCG@20, ensuring fairness in model performance across different user subgroups.
Hackernoonhttps://hackernoon.com/countering-mainstream-bias-via-end-to-end-adaptive-local-learning-debiasing-experiments-and-setup
A comprehensive experimental setup adheres to the Rawlsian Max-Min fairness principle to balance performance for both high and low mainstream user groups.
Hackernoonhttps://hackernoon.com/countering-mainstream-bias-via-end-to-end-adaptive-local-learning-debiasing-experiments-and-setup
Our debiasing method, TALL, is benchmarked against successful methods like MultVAE and local learning techniques to validate its effectiveness and model components.
Hackernoonhttps://hackernoon.com/countering-mainstream-bias-via-end-to-end-adaptive-local-learning-debiasing-experiments-and-setup
The analysis includes impact evaluations of the adaptive weight module, ablation studies, and hyper-parameter variations to reinforce robustness of findings.
Hackernoonhttps://hackernoon.com/countering-mainstream-bias-via-end-to-end-adaptive-local-learning-debiasing-experiments-and-setup
Read at Hackernoon
#debiasing #machine-learning #adaptive-learning #fairness #recommendation-systems
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