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cs.IR2024
Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation
Luo Ji, Gao Liu, Mingyang Yin +2
Modern listwise recommendation systems need to consider both long-term user perceptions and short-term interest shifts. Reinforcement learning can be applied on recommendation to s…
cs.CL2024
A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio
Ningyuan Xi, Yetao Wu, Kun Fan +3
Large Language Models (LLM) often need to be Continual Pre-Trained (CPT) to obtain unfamiliar language skills or adapt to new domains. The huge training cost of CPT often asks for…
cs.IR2024
Causal Distillation for Alleviating Performance Heterogeneity in Recommender Systems
Shengyu Zhang, Ziqi Jiang, Jiangchao Yao +7
Recommendation performance usually exhibits a long-tail distribution over users -- a small portion of head users enjoy much more accurate recommendation services than the others. W…