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
this https URL

本文提出了一种基于对比学习的 CV R 预测框架，使用大量无标签数据学习更好的数据表示，改善预测性能。通过嵌入掩蔽、虚假否定排除和监督正样本包含等方法，削弱了数据稀疏性的限制，实验证明其在真实世界的转化数据集上具有优越表现。