15 citations · 15 across the 4 of their papers we have counts for
4 papers · 1 filter
UMRE: A Unified Monotonic Transformation for Ranking Ensemble in Recommender Systems
Zhengrui Xu, Zhe Yang, Zhengxiao Guo +5
Industrial recommender systems commonly rely on ensemble sorting (ES) to combine predictions from multiple behavioral objectives. Traditionally, this process depends on manually de…
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…
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…
CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation
Chong Liu, Xiaoyang Liu, Rongqin Zheng +6
Sequential recommendation methods are increasingly important in cutting-edge recommender systems. Through leveraging historical records, the systems can capture user interests and…