7 citations · 9 across the 9 of their papers we have counts for
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cs.IR2023
Learning from All Sides: Diversified Positive Augmentation via Self-distillation in Recommendation
Chong Liu, Xiaoyang Liu, Ruobing Xie +3
Personalized recommendation relies on user historical behaviors to provide user-interested items, and thus seriously struggles with the data sparsity issue. A powerful positive ite…
cs.IR2022
UFNRec: Utilizing False Negative Samples for Sequential Recommendation
Xiaoyang Liu, Chong Liu, Pinzheng Wang +5
Sequential recommendation models are primarily optimized to distinguish positive samples from negative ones during training in which negative sampling serves as an essential compon…