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Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation
Chong Liu, Xiaoyang Liu, Lixin Zhang +2
Recommendation systems rely on historical clicks to learn user interests and provide appropriate items. However, current studies tend to treat clicks equally, which may ignore the…
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…
Single-shot Embedding Dimension Search in Recommender System
Liang Qu, Yonghong Ye, Ningzhi Tang +3
As a crucial component of most modern deep recommender systems, feature embedding maps high-dimensional sparse user/item features into low-dimensional dense embeddings. However, th…