The adaptive weight module dynamically assigns weights to synchronize learning paces, facilitating higher performance for mainstream users while gradually shifting focus to niche users as training progresses.
Experimentation on the ML1M dataset shows that initially higher weights are assigned to mainstream users due to their easier learning curve, before transitioning to niche users for improved overall model performance.
#adaptive-learning #machine-learning #user-synchronization #hyper-parameter-tuning #debiasing-techniques
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