3 papers
cs.LG2024
Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction
Yun Yue, Yongchao Liu, Suo Tong +7
We develop a novel framework that adds the regularizers of the sparse group lasso to a family of adaptive optimizers in deep learning, such as Momentum, Adagrad, Adam, AMSGrad, Ada…
cs.IR2024
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation
Shaowei Wei, Zhengwei Wu, Xin Li +5
Sequential recommendation methods play a pivotal role in modern recommendation systems. A key challenge lies in accurately modeling user preferences in the face of data sparsity. T…
cs.IR2024
Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph
Qian Zhao, Hao Qian, Ziqi Liu +2
Recommendation systems are widely used in e-commerce websites and online platforms to address information overload. However, existing systems primarily rely on historical data and…