AutoHERI

Abstract

Post-click conversion rate (CVR) estimation is a crucial task in online advertising and recommendation systems. To address the sample selection bias problem in traditional CVR models trained in click space, recent studies perform entire space multi-task learning based on the probability of events in user behavior funnels like impression-click-conversion. However, those models learn the feature representation of each task independently, and omit potential inter-task correlations that can help improve the CVR estimation performance. In this paper, we propose AutoHERI, an entire space CVR model with automated hierarchical representation integration, which leverages the interplay across multi-tasks' representation learning. It performs neural architecture search to learn optimal connections between layer-wise representations of different tasks. Besides, AutoHERI achieves better search efficiency with one-shot search algorithm, and thus it can be easily extended to new scenarios that have more complex user behaviors. Both offline and online experimental results on large-scale real-world datasets verify that AutoHERI outperforms previous entire space models significantly.