AINeutralarXiv – CS AI · Mar 34/103
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Rejuvenating Cross-Entropy Loss in Knowledge Distillation for Recommender Systems
Researchers propose Rejuvenated Cross-Entropy for Knowledge Distillation (RCE-KD) to improve knowledge distillation in recommender systems by addressing limitations of Cross-Entropy loss when distilling teacher model rankings. The method splits teacher's top items into subsets and uses adaptive sampling to better align with theoretical assumptions.