Contrastive Learning for Conversion Rate Prediction
Wentao Ouyang Rui Dong Xiuwu Zhang Chaofeng Guo Jinmei Luo Xiangzheng Liu Yanlong Du
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.