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Improved Online Learning Algorithms for CTR Prediction in Ad Auctions
March 5, 2024, 2:43 p.m. | Zhe Feng, Christopher Liaw, Zixin Zhou
cs.LG updates on arXiv.org arxiv.org
Abstract: In this work, we investigate the online learning problem of revenue maximization in ad auctions, where the seller needs to learn the click-through rates (CTRs) of each ad candidate and charge the price of the winner through a pay-per-click manner. We focus on two models of the advertisers' strategic behaviors. First, we assume that the advertiser is completely myopic; i.e.~in each round, they aim to maximize their utility only for the current round. In this …
abstract algorithms arxiv click cs.gt cs.ir cs.lg focus learn online learning per prediction price revenue through type work
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