Improving conversion rate prediction via self-supervised pre-training in online advertising

The task of predicting conversion rates (CVR) lies at the heart of online advertising systems aiming to optimize bids to meet advertiser performance requirements. Even with the recent rise of deep neural networks, these predictions are often made by factorization machines (FM), especially in commercial settings where inference latency is key. These models are trained using the logistic regression framework on labeled tabular data formed from past user activity that is relevant to the task at hand.
Many advertisers only …

advertising commercial conversion conversion rates cs.ir cs.lg factorization inference latency lies machines networks neural networks online advertising performance prediction predictions pre-training rate requirements stat.ml systems training via