只在合适时机调用大模型，提升推荐系统准确性
Selective LLM-Guided Regularization for Enhancing Recommendation Models
- 按用户历史、物品热度和模型置信度决定是否启用大模型指导
- 冷启动与长尾场景下准确率显著提升，优于全局知识蒸馏
- 离线生成大模型评分，不增加推理开销，适合实际部署
大语言模型具备丰富的语义先验和强大的推理能力，是推荐系统的潜在辅助信号。然而，现有方法要么将大模型作为独立推荐器（成本高、偏差大），要么采用全局知识蒸馏（强制模型模仿可能错误的预测）。近期研究发现，大模型在重排和挑战性场景中表现更优，而非所有情境都适用。本文提出选择性大模型引导正则化框架，仅当可训练门控机制基于用户历史长度、物品流行度和模型不确定性判断大模型可靠时，才激活其成对排序监督。所有大模型打分均离线完成，不增加推理成本。多数据集实验表明，该选择性策略持续提升整体准确率，在冷启动和长尾场景下收益显著，优于全局蒸馏基线。
原文摘要 · Abstract (English)
Large language models provide rich semantic priors and strong reasoning capabilities, making them promising auxiliary signals for recommendation. However, prevailing approaches either deploy LLMs as standalone recommender or apply global knowledge distillation, both of which suffer from inherent drawbacks. Standalone LLM recommender are costly, biased, and unreliable across large regions of the user item space, while global distillation forces the downstream model to imitate LLM predictions even when such guidance is inaccurate. Meanwhile, recent studies show that LLMs excel particularly in re-ranking and challenging scenarios, rather than uniformly across all contexts.We introduce Selective LLM Guided Regularization, a model-agnostic and computation efficient framework that activates LLM based pairwise ranking supervision only when a trainable gating mechanism informing by user history length, item popularity, and model uncertainty predicts the LLM to be reliable. All LLM scoring is performed offline, transferring knowledge without increasing inference cost. Experiments across multiple datasets show that this selective strategy consistently improves overall accuracy and yields substantial gains in cold start and long tail regimes, outperforming global distillation baselines.
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