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Improving conversion rate prediction via self-supervised pre-training in online advertising. (arXiv:2401.16432v1 [cs.IR])
cs.LG updates on arXiv.org arxiv.org
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 …
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