论文标题
Creater：由CTR驱动的广告文本生成，具有受控的预培训和对比度微调
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning
论文作者
论文摘要
本文着重于自动生成AD的文本，目标是生成的文本可以捕获用户利益，以实现更高的点击率（CTR）。我们建议Creater是一种CTR驱动的广告文本生成方法，以根据高质量用户评论生成广告文本。为了结合CTR目标，我们的模型从在线A/B测试数据中学习了对比度学习，这鼓励模型生成获得更高CTR的广告文本。为了减轻低资源问题，我们设计了一个定制的自我监督的目标，以减少培训和微调之间的差距。工业数据集的实验表明，Creater明显优于当前方法。它已在网上部署在领先的广告平台中，并在核心在线指标上提升。
This paper focuses on automatically generating the text of an ad, and the goal is that the generated text can capture user interest for achieving higher click-through rate (CTR). We propose CREATER, a CTR-driven advertising text generation approach, to generate ad texts based on high-quality user reviews. To incorporate CTR objective, our model learns from online A/B test data with contrastive learning, which encourages the model to generate ad texts that obtain higher CTR. To alleviate the low-resource issue, we design a customized self-supervised objective reducing the gap between pre-training and fine-tuning. Experiments on industrial datasets show that CREATER significantly outperforms current approaches. It has been deployed online in a leading advertising platform and brings uplift on core online metrics.
