Post-click Conversion Rate (CVR) prediction task plays an essential role in industrial applications, such as recommendation and advertising. Conventional CVR methods typically suffer from the data sparsity problem as they rely only on samples where the user has clicked. To address this problem, researchers have introduced the method of multi-task learning, which utilizes non-clicked samples and shares feature representations of the Click-Through Rate (CTR) task with the CVR task. However, it should be noted that the CVR and CTR tasks are fundamentally different and may even be contradictory. Therefore, introducing a large amount of CTR information without distinction may drown out valuable information related to CVR. This phenomenon is called the curse of knowledge problem in this paper. To tackle this issue, we argue that a trade-off should be achieved between the introduction of large amounts of auxiliary information and the protection of valuable information related to CVR. Hence, we propose a Click-aware Structure Transfer model with sample Weight Assignment, abbreviated as CSTWA. It pays more attention to the latent structure information, which can filter the input information that is related to CVR, instead of directly sharing feature representations. Meanwhile, to capture the representation conflict between CTR and CVR, we calibrate the representation layer and reweight the discriminant layer to excavate the click bias information from the CTR tower. Moreover, it incorporates a sample weight assignment algorithm biased towards CVR modeling, to make the knowledge from CTR would not mislead the CVR. Extensive experiments on industrial and public datasets have demonstrated that CSTWA significantly outperforms widely used and competitive models.

翻译：点击后转化率（CVR）预测任务在推荐和广告等工业应用中扮演着关键角色。传统CVR方法通常仅依赖用户已点击的样本，因此面临数据稀疏性问题。为解决该问题，研究者引入了多任务学习方法，利用未点击样本，并将点击率（CTR）任务的特征表示与CVR任务共享。然而，需注意CVR与CTR任务存在本质差异，甚至可能相互矛盾。因此，不加区分地引入大量CTR信息可能淹没与CVR相关的有价值信息，本文将此现象称为知识诅咒问题。为应对这一挑战，我们认为应在引入大量辅助信息与保护CVR相关有价值信息之间达成权衡。为此，我们提出了一种带样本加权分配的点击感知结构迁移模型，简称CSTWA。该模型更关注潜在结构信息，能够筛选与CVR相关的输入信息，而非直接共享特征表示。同时，为捕捉CTR与CVR之间的表示冲突，我们对表示层进行校准，并对判别层重新加权，以从CTR塔中挖掘点击偏差信息。此外，模型引入了偏向CVR建模的样本权重分配算法，使CTR知识不会误导CVR。在工业与公开数据集上的大量实验表明，CSTWA显著优于广泛使用的竞争模型。