区分点击意图，让广告转化预测更准。
MARCO: Click-Intent Decomposition for Calibrated Ads Conversion Prediction
- 按用户点击行为分解意图，训练不同意图的转化率模型。
- 线上部署后每点击转化率提升2.80%，整体指标增0.98%。
- 适合大规模广告系统优化，尤其关注精准归因与校准。
并非所有点击都等价。工业级广告排序将转化率拆分为点击率（CTR）与点击后转化率（CVR），但默认所有点击相同。实际上，用户通过界面交互自发传递意图信号，同一广告的不同点击类型实际转化率相差4倍。传统CVR模型混淆此类信号，导致高意图点击预测偏低、低意图点击预测偏高，这种偏差被整体校准度接近完美的现象掩盖。本文提出MARCO（多意图广告排序组合优化）框架，通过解析点击意图实现去偏：利用日志中的点击类型作为免费行为标签，对同质群体训练各意图独立的CVR头，并在推理时根据意图分布组合各头预测。理论上证明分解不会增加总体风险，在平方损失下有明确改进空间，且在线上路由效率控制下可实现部分收益。由于总体最优得分不变，所有提升均为有限容量下的估计与校准改进，经离线与线上验证有效。为规模化部署，进一步将多曝光、多点击归因建模为带偏差-方差权衡的信用分配问题，证明在生产约束下，末次点击、首次点击归因是最优的低偏差、低方差确定性选择，并推导出三个端到端强制的一致性条件。在二元意图粒度部署后，各意图校准逼近100%，每点击转化率提升+2.80%，整体核心指标提升+0.98%。
原文摘要 · Abstract (English)
Not all clicks are equal. Industrial ads ranking decouples conversion probability into click-through rate (CTR) and post-click conversion rate (CVR), yet treats every click as the same event. In reality, users provide a free, self-generated signal of intent through their physical UI interactions. Different click types on the same ad exhibit a 4-fold difference in actual conversion rates. By conflating these signals, the standard CVR model under-predicts high-intent clicks and over-predicts low-intent ones, which is a bias masked by near-perfect aggregate calibration. We propose MARCO (Multi-intent Ads Ranking Composition Optimization), a framework that resolves this bias by decomposing each click by intent. Using the logged click type as a free behavioral label, MARCO trains per-intent CVR heads on homogeneous populations, and at serving time composes their per-intent CVR estimates under a predicted distribution over intents. Theoretically, we prove that decomposition never raises population risk, give the exact headroom under squared loss and non-negativity under the deployed loss, and show through a routing-efficiency dial how much of it reaches serving. Because the population-optimal score is unchanged, any gain is a finite-capacity estimation and calibration effect that we validated both offline and online. For deployment at scale, we further cast multi-impression, multi-click attribution as credit assignment with a bias-variance tradeoff analogous to RL return estimation, showing last-impression, first-click attribution is the low-bias, low-variance, deterministic choice under production constraints, and derive three consistency conditions enforced end-to-end at scale. Deployed at binary intent granularity, MARCO corrects per-intent calibration to approximately 100%, lifts conversions per click by +2.80%, and drives +0.98% cumulative improvement in topline metrics.
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