用大模型识别电商转化中被忽略的用户触点，提升推荐精准度。
Can Large Language Models Identify Meaningful Touchpoints in Conversion Attribution?
- 用大模型分析用户行为中的隐含语义关联，替代传统规则筛选触点。
- 大模型能发现大量被传统方法遗漏的有意义触点，但仍有提升空间。
- 适合做广告推荐、转化归因优化的研究者与工业界从业者。
转化归因中的触点选择，即识别对转化有贡献的有意义触点，对电商推荐和在线广告至关重要。现有方法严重依赖基于协同过滤的启发式规则，难以匹配用户实际的语义意图。通过人工标注，我们发现存在显著语义差距：许多隐含相关且语义相关的触点未被现有规则捕捉。因此，我们系统评估了大语言模型（LLMs）在识别这些隐藏关联方面的能力。结果显示，尽管大模型能有效发现大量隐含相关触点，其选择性能仍有较大提升空间。此外，我们分析了不同提示策略和基础模型选择对识别效果的影响，揭示了其推理模式与有效性。这些洞察为将转化归因从机械规则匹配转向人类对齐的语义推理提供了新路径。更重要的是，我们利用大模型生成的转化标签增强工业级CVR模型训练，实现显著的离线性能提升，展示了大模型在转化归因中的潜力。
原文摘要 · Abstract (English)
Touchpoint selection in conversion attribution, namely identifying meaningful touchpoints contributing to conversions, is essential for e-commerce recommendation and online advertising. Current selection methods rely heavily on collaborative-filtering-based heuristics, which fail to align with user-perceived semantic intent. Through human annotation, we reveal a significant semantic gap: many implicitly-related, semantically relevant touchpoints remain undetected by existing rules. Therefore, we systematically evaluate the capability of Large Language Models (LLMs) in identifying these hidden associations. Our evaluation shows that while LLMs effectively uncover a substantial portion of implicitly-related touchpoints, significant room for improvement remains in their selection performance. Furthermore, we analyze the impact of different prompting strategies and foundation model choices on identification performance, providing valuable insights into their reasoning patterns and effectiveness. These insights offer a new roadmap for transitioning conversion attribution from mechanical rule-matching to human-aligned semantic reasoning. Moreover, we leverage the LLM-attributed conversion labels for enhancing industrial CVR model training and achieve significant offline performance gains, showing the potential of LLMs in conversion attribution.
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