基于反事实推理的点击后转化率预测研究
On Predicting Post-Click Conversion Rate via Counterfactual Inference
AI总结：
针对点击样本稀疏导致的CVR预测偏差问题，提出基于因果反事实推理的ESCIM模型，通过为未点击样本生成反事实转化标签扩充训练数据，在公开数据集和真实场景A/B测试中均取得更优效果。
摘要：
准确预测转化率（CVR）在在线广告系统、电商等各类推荐场景中至关重要。这类系统使用包含曝光、点击、转化行为的用户交互日志数据。由于转化行为仅发生在点击之后，CVR预测模型通常仅基于点击样本进行训练，但点击样本的稀疏性使得模型训练需要收集海量日志数据。近期研究通过构建利用未点击样本的框架来解决这一问题，这类框架旨在缓解点击样本与未点击样本的分布差异带来的偏差，但大多依赖启发式方法。\n在此背景下，我们以因果关系为指导原则，提出一种为未点击样本反事实生成转化标签的方法，尝试回答“如果用户点击了推荐商品，是否会发生转化？”这一问题。该方法名为全空间反事实推理多任务模型（Entire Space Counterfactual Inference Multi-task Model, ESCIM）。我们首先训练用户序列行为的结构因果模型（structural causal model, SCM），对未点击商品施加假设干预（即点击操作），以推断反事实CVR；随后提出多种方法将预测得到的反事实CVR转换为未点击样本的二值反事实转化标签；最后将生成的样本纳入模型训练流程。\n在公开数据集上的大量实验证明了所提算法的优越性。在线A/B测试进一步验证了该算法在真实场景中的有效性。此外，我们还证明了该方法在潜在转化数据上的性能提升，展现了其鲁棒性与优异的泛化能力。
英文摘要：
Accurately predicting conversion rate (CVR) is essential in various recommendation domains such as online advertising systems and e-commerce. These systems utilize user interaction logs, which consist of exposures, clicks, and conversions. CVR prediction models are typically trained solely based on clicked samples, as conversions can only be determined following clicks. However, the sparsity of clicked instances necessitates the collection of a substantial amount of logs for effective model training. Recent works address this issue by devising frameworks that leverage non-clicked samples. While these frameworks aim to reduce biases caused by the discrepancy between clicked and non-clicked samples, they often rely on heuristics. Against this background, we propose a method to counterfactually generate conversion labels for non-clicked samples by using causality as a guiding principle, attempting to answer the question, "Would the user have converted if he or she had clicked the recommended item?" Our approach is named the Entire Space Counterfactual Inference Multi-task Model (ESCIM). We initially train a structural causal model (SCM) of user sequential behaviors and conduct a hypothetical intervention (i.e., click) on non-clicked items to infer counterfactual CVRs. We then introduce several approaches to transform predicted counterfactual CVRs into binary counterfactual conversion labels for the non-clicked samples. Finally, the generated samples are incorporated into the training process. Extensive experiments on public datasets illustrate the superiority of the proposed algorithm. Online A/B testing further empirically validates the effectiveness of our proposed algorithm in real-world scenarios. In addition, we demonstrate the improved performance of the proposed method on latent conversion data, showcasing its robustness and superior generalization capabilities.
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