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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★ 1 cited
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…