Conversion rate (CVR) prediction plays an important role in advertising systems. Recently, supervised deep neural network-based models have shown promising performance in CVR prediction. However, they are data hungry and require an enormous amount of training data. In online advertising systems, although there are millions to billions of ads, users tend to click only a small set of them and to convert on an even smaller set. This data sparsity issue restricts the power of these deep models. In this paper, we propose the Contrastive Learning for CVR prediction (CL4CVR) framework. It associates the supervised CVR prediction task with a contrastive learning task, which can learn better data representations exploiting abundant unlabeled data and improve the CVR prediction performance. To tailor the contrastive learning task to the CVR prediction problem, we propose embedding masking (EM), rather than feature masking, to create two views of augmented samples. We also propose a false negative elimination (FNE) component to eliminate samples with the same feature as the anchor sample, to account for the natural property in user behavior data. We further propose a supervised positive inclusion (SPI) component to include additional positive samples for each anchor sample, in order to make full use of sparse but precious user conversion events. Experimental results on two real-world conversion datasets demonstrate the superior performance of CL4CVR. The source code is available at https://github.com/DongRuiHust/CL4CVR.

翻译：转化率（CVR）预测在广告系统中扮演着重要角色。近年来，基于监督深度神经网络的模型在CVR预测中展现出良好性能。然而，这些模型对数据需求量大，需要海量训练数据。在在线广告系统中，虽然存在数百万到数十亿广告，但用户往往只点击其中极少部分，转化量更少。这种数据稀疏性问题限制了深度模型的能力。本文提出用于CVR预测的对比学习框架（CL4CVR）。该框架将监督式CVR预测任务与对比学习任务相结合，通过利用大量无标签数据学习更好的数据表示，从而提升CVR预测性能。为将对比学习任务适配到CVR预测问题，我们提出嵌入掩码（EM）而非特征掩码来生成增强样本的两个视图。同时，引入假负例消除（FNE）组件，消除与锚点样本特征相同的样本，以顺应行为数据的自然特性。进一步提出监督式正例包含（SPI）组件，为每个锚点样本增加额外正样本，从而充分利用稀疏而珍贵的用户转化事件。在两个真实转化数据集上的实验结果表明，CL4CVR具有优越性能。源代码已开源至https://github.com/DongRuiHust/CL4CVR。