Click-Through Rate (CTR) prediction serves as a fundamental component in online advertising. A common practice is to train a CTR model on advertisement (ad) impressions with user feedback. Since ad impressions are purposely selected by the model itself, their distribution differs from the inference distribution and thus exhibits sample selection bias (SSB) that affects model performance. Existing studies on SSB mainly employ sample re-weighting techniques which suffer from high variance and poor model calibration. Another line of work relies on costly uniform data that is inadequate to train industrial models. Thus mitigating SSB in industrial models with a uniform-data-free framework is worth exploring. Fortunately, many platforms display mixed results of organic items (i.e., recommendations) and sponsored items (i.e., ads) to users, where impressions of ads and recommendations are selected by different systems but share the same user decision rationales. Based on the above characteristics, we propose to leverage recommendations samples as a free lunch to mitigate SSB for ads CTR model (Rec4Ad). After elaborating data augmentation, Rec4Ad learns disentangled representations with alignment and decorrelation modules for enhancement. When deployed in Taobao display advertising system, Rec4Ad achieves substantial gains in key business metrics, with a lift of up to +6.6\% CTR and +2.9\% RPM.

翻译：点击率预测是在线广告中的基础组件。常见做法是基于广告曝光及用户反馈训练点击率模型。由于广告曝光由模型自身刻意选择，其分布与推理分布存在差异，从而产生影响模型性能的样本选择偏差。现有针对样本选择偏差的研究主要采用样本重加权技术，但该方法存在高方差和模型校准效果差的问题。另一类研究依赖于成本高昂的均匀采样数据，难以训练工业级模型。因此，在无均匀数据框架下缓解工业级模型的样本选择偏差值得探索。幸运的是，许多平台向用户混合展示自然结果（即推荐内容）和商业结果（即广告内容），其中广告和推荐的曝光由不同系统选择，但共享相同的用户决策逻辑。基于上述特征，我们提出利用推荐样本作为免费午餐来缓解广告点击率模型的样本选择偏差（Rec4Ad）。在精炼数据增强后，Rec4Ad通过对齐与解耦模块学习解耦表示以增强模型性能。在淘宝展示广告系统中部署时，Rec4Ad在关键业务指标上取得显著提升，CTR提升高达+6.6%，RPM提升+2.9%。