- Boxiang Lyu
- Sanmi Koyejo
- Zachary Robertson
- Zhe Feng
We study the design of loss functions for click-through rates (CTR) to optimize (social) welfare in ad auctions. Existing works either only focus on CTR predictions without consideration of business objectives (e.g. welfare) in auctions or assume that the distribution over the participants' expected cost-per-impression (eCPM) is known a priori and uses various additional assumptions on the parametric form of the distribution to derive loss functions for predicting CTRs. In this work, we bring back the welfare objectives of ad auctions into CTR predictions and propose a novel weighted rankloss to train CTR model. Compared with the previous literature, our approach provides a provable guarantee on welfare but without any assumptions on the eCPMs' distribution, while also avoiding the intractability of naively applying existing learning-to-rank methods. We further propose a theoretically justifiable technique for calibrating the losses using labels generated from a teacher network, only assuming that the teacher network has bounded $\ell_2$ generalization error. Finally, we demonstrate the practical advantages of the proposed loss on both synthetic and real-world data.
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