Adaptive$^2$: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling

TL;DR

Adaptive$^2$ introduces an automatic domain mining framework for fine-grained domain adaptation in advertising, outperforming traditional methods reliant on hand-crafted domain info and demonstrating practical benefits in live systems.

Contribution

The paper presents Adaptive$^2$, a novel framework that automatically learns domain patterns using self-supervision and applies shared&specific networks for improved adaptation.

Findings

Adaptive$^2$ outperforms existing domain adaptation methods on benchmarks.

Traditional methods with hand-crafted domains do not outperform single-domain models.

Adaptive$^2$ shows commercial value in live advertising system deployment.

Abstract

Advertising systems often face the multi-domain challenge, where data distributions vary significantly across scenarios. Existing domain adaptation methods primarily focus on building domain-adaptive neural networks but often rely on hand-crafted domain information, e.g., advertising placement, which may be sub-optimal. We think that fine-grained "domain" patterns exist that are difficult to hand-craft in online advertisement. Thus, we propose Adaptive$^2$, a novel framework that first learns domains adaptively using a domain mining module by self-supervision and then employs a shared&specific network to model shared and conflicting information. As a practice, we use VQ-VAE as the domain mining module and conduct extensive experiments on public benchmarks. Results show that traditional domain adaptation methods with hand-crafted domains perform no better than single-domain models under fair FLOPS conditions, highlighting the importance of domain definition. In contrast, Adaptive$^2$ outperforms existing approaches, emphasizing the effectiveness of our method and the significance of domain mining. We also deployed Adaptive$^2$ in the live streaming scenario of Kuaishou Advertising System, demonstrating its commercial value and potential for automatic domain identification. To the best of our knowledge, Adaptive$^2$ is the first approach to automatically learn both domain identification and adaptation in online advertising, opening new research directions for this area.