BAN:Large Scale Brand ANonymization for Creative Recommendation Via Label Light Adaptation

November 15, 2021

Abstract

One of the primary component in ads creative recommen- dation system is the brand anonymization that removes brand-specific information from ad text for legal compliance and providing ready-to-use template for the advertisers to customize and consume. In our previous work on ads creative recommendation system, the anonymization is done via a block list created solely based on manual reviewing, which is expensive and limits in the scale of the deployment of the ads recommendation. In this work we investigate a large scale, automated approach for brand anonymization. Such a problem presents many unique and non-trivial challenges, including the domain specificity of the brand entities, the fine-granularity requirements of structured output, the tight constraint of the limited contexts, the high level of grammatical noise in the advertisement data, and the heterogeneity of information required to perform anonymization. We propose a transformer model that leverage implicit knowledge together with a label-light adaptation procedure for this task. Our model is rolled out to ads systems in Yahoo that cover billions of impression traffic per month and improved previous production system by 68.3% F1-score on token level prediction and 61.6% on ad level prediction.\

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Publication Type

Paper

Conference / Journal Name

IEEE Big Data 2021 (Poster)

Authors

BibTeX


@inproceedings{
    author = {},
    title = {‌BAN:Large Scale Brand ANonymization for Creative Recommendation Via Label Light Adaptation‌},
    booktitle = {Proceedings of IEEE Big Data 2021 (Poster)‌},
    year = {‌2021‌}
}