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Zhejiang University
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Length Bias in LLM-based recommendations can be effectively mitigated, leading to a 16.82% improvement in accuracy and fairness without significant computational costs.
LLMs for recommendation can now surpass the limitations of static training signals, achieving sustained improvements in ranking accuracy, fairness, and diversity through a dynamically updated Bayesian distillation target.
