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

• URL: http://arxiv.org/abs/2412.08198v1
• Date: Wed, 11 Dec 2024 08:41:41 GMT
• Title: Adaptive$^2$: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling
• Authors: Wenxuan Sun, Zixuan Yang, Yunli Wang, Zhen Zhang, Zhiqiang Wang, Yu Li, Jian Yang, Yiming Yang, Shiyang Wen, Peng Jiang, Kun Gai,
• Abstract summary: We propose Adaptive$2$, a novel framework that learns domains adaptively using a domain mining module by self-supervision. Results show that traditional domain adaptation methods with hand-crafted domains perform no better than single-domain models under fair FLOPS conditions. Adaptive$2$ is the first approach to automatically learn both domain identification and adaptation in online advertising.
• Score: 50.85199749890184
• License:
• 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.

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