Improving Conversion Rate Prediction via Self-Supervised Pre-Training in Online Advertising

October 28, 2023

Abstract

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.

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Publication Type

Paper

Conference / Journal Name

IEEE International Conference on Big Data

Authors

Alex Shtoff

Yohay Kaplan

Ariel Raviv

BibTeX


@inproceedings{
    author = {},
    title = {‌Improving Conversion Rate Prediction via Self-Supervised Pre-Training in Online Advertising‌},
    booktitle = {Proceedings of IEEE International Conference on Big Data‌},
    year = {‌2023‌}
}