发表机构
Tsinghua University; Meituan（清华大学; 美团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电子商务平台新用户冷启动预测难题，SemRaD框架利用结构化语义推理管道和事后感知蒸馏网络，有效弥合信息差距，提升了LTV和CVR，减少训练数据用量，在工业数据集和在线测试中均取得良好效果。
AI 中文摘要
新用户冷启动是电子商务平台的关键瓶颈，即预测交互历史稀疏用户的终身价值（LTV）和转化率（CVR）。基于大语言模型的语义增强和使用特权信息学习这两个先前方向各有局限。本文提出SemRaD框架，通过结构化语义推理管道生成致密化语义配置文件和事后蒸馏目标，利用事后感知蒸馏网络传递特权知识。在大规模工业数据集上，SemRaD提升了LTV和CVR，在Keeta的四周在线A/B测试也有成效，还能用更少训练数据匹配生产系统的LTV并提升CVR。
英文摘要
New-user cold-start is a critical bottleneck for e-commerce platforms: predicting user lifetime value (LTV) and conversion rate (CVR) for users with sparse interaction history. Two prior directions -- LLM-based semantic augmentation and learning using privileged information (LUPI) -- each face a key limitation. First, LLM augmentation produces unstructured rationales that are noisy and hard to operationalize in production. Second, naive student-teacher distillation can be brittle due to an information gap between the privileged teacher and the sparse student; moreover, this gap is heterogeneous across users. We propose SemRaD, a Semantic Reasoning-aware Distillation framework addressing both limitations. First, a Structured Semantic Reasoning Pipeline replaces free-form rationales with a structured schema built via a discover-curate-audit workflow, producing per user a Densified Semantic Profile (consumed by the deployed student via a Semantic-Gated Encoder that focuses on the most informative dimensions) and a Hindsight Distillation Target reconciled from pre- and post-conversion reasoning (used only at training). Second, to bridge this gap and handle its heterogeneity, a Hindsight-Aware Distillation Network transfers privileged knowledge via the hindsight target, with Distillation Experts improving transfer under per-user variability. On a large-scale industrial dataset, SemRaD lifts +1.9% LTV (Gini) and +1.0% CVR (AUROC) over a production-grade base; a four-week online A/B at Keeta confirms +1.0% LTV / +0.43% CVR. SemRaD also matches the production system's LTV using only 9% of the training data while improving CVR by 0.8%.
