用语义聚类和不确定性感知提升大模型推荐准确性
Uncertainty-Aware Semantic Decoding for LLM-Based Sequential Recommendation
- 将相似物品的预测分数聚类，动态调整推荐权重
- 在6个亚马逊数据集上，点击率提升18.5%，排序效果提升超10%
- 适用于不同领域推荐，尤其适合追求高精度的工业场景
大语言模型广泛用于序列推荐任务，但推理时仍沿用自然语言生成的解码策略，导致文本生成目标与推荐目标不匹配。本文提出不确定性感知语义解码（USD）框架，通过基于logit的聚类与自适应评分机制改进下一物品预测。该方法将具有相似logit向量的物品聚为语义等价组，重新分配组内概率质量，并计算组间熵以调控推荐推理中的评分与采样温度。在亚马逊产品数据集（六个领域）上的实验显示，相较于先进基线，HR@3提升18.5%，NDCG@3提升11.9%，MRR@3提升10.8%。超参数分析验证了最优配置的有效性，且在H&M与Netflix数据集上的测试表明该框架可适配不同推荐领域。结果证实，融合语义聚类与不确定性评估能显著提升推荐的可靠性和准确性。
原文摘要 · Abstract (English)
Large language models have been widely applied to sequential recommendation tasks, yet during inference, they continue to rely on decoding strategies developed for natural language processing. This creates a mismatch between text-generation objectives and recommendation next item selection objectives. This paper addresses this limitation by proposing an Uncertainty-aware Semantic Decoding (USD) framework that combines logit-based clustering with adaptive scoring to improve next-item predictions. Our approach clusters items with similar logit vectors into semantic equivalence groups, then redistributes probability mass within these clusters and computes entropy across them to control item scoring and sampling temperature during recommendation inference. Experiments on Amazon Product datasets (six domains) gains of 18.5\% in HR@3, 11.9\% in NDCG@3, and 10.8\% in MRR@3 compared to state-of-the-art baselines. Hyperparameter analysis confirms the optimal parameters among various settings, and experiments on H\&M, and Netflix datasets indicate that the framework can adapt to differing recommendation domains. The experimental results confirm that integrating semantic clustering and uncertainty assessment yields more reliable and accurate recommendations.
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