Adaptive$^2$: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915193557090304 |
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| author | Sun, Wenxuan Yang, Zixuan Wang, Yunli Zhang, Zhen Wang, Zhiqiang Li, Yu Yang, Jian Yang, Yiming Wen, Shiyang Jiang, Peng Gai, Kun |
| author_facet | Sun, Wenxuan Yang, Zixuan Wang, Yunli Zhang, Zhen Wang, Zhiqiang Li, Yu Yang, Jian Yang, Yiming Wen, Shiyang Jiang, Peng Gai, Kun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08198 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive$^2$: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling Sun, Wenxuan Yang, Zixuan Wang, Yunli Zhang, Zhen Wang, Zhiqiang Li, Yu Yang, Jian Yang, Yiming Wen, Shiyang Jiang, Peng Gai, Kun Machine Learning I.2.6; H.3.3 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. |
| title | Adaptive$^2$: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling |
| topic | Machine Learning I.2.6; H.3.3 |
| url | https://arxiv.org/abs/2412.08198 |