学习创造更好的广告：广告创意改进的生成和排名方法
Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement
摘要：
在在线广告行业中，设计广告创意的过程（即，广告文本和图像）需要手工劳动。通常，每个广告主通过在线A/B测试推出多个创意，以推断目标受众的有效创意，然后以迭代的方式进一步细化。由于此过程的手动性质，学习、改进和部署修改后的创意非常耗时。由于主要的广告平台通常并行运行多个广告主的A/B测试，我们探索通过多个广告主的A/B测试协作学习广告创意改进的可能性。特别是，给定一个输入广告创意，我们研究的方法来细化给定的广告文本和图像：（i）生成新的广告文本，（ii）推荐新的广告文本的关键词，以及（iii）推荐图像标签（图像中的对象），以选择新的广告图像。基于多个广告商进行的A/B测试，我们形成了劣质和优质广告创意的成对示例，并使用这些对来训练模型以完成上述任务。为了生成新的广告文本，我们展示了具有复制机制的编码器-解码器架构的功效，该机制允许将（较差的）输入文本中的一些单词复制到输出中，同时合并与更高点击率相关的新单词。对于关键词和图像标签推荐任务，我们证明了深度相关性匹配模型的有效性，以及与看不见的广告客户的冷启动场景中的广告文本生成相比，排名方法的相对鲁棒性。我们还分享了我们使用Yahoo Gemini广告平台数据进行的实验中获得的广泛适用的见解。
英文摘要：
In the online advertising industry, the process of designing an ad creative (i.e., ad text and image) requires manual labor. Typically, each advertiser launches multiple creatives via online A/B tests to infer effective creatives for the target audience, that are then refined further in an iterative fashion. Due to the manual nature of this process, it is time-consuming to learn, refine, and deploy the modified creatives. Since major ad platforms typically run A/B tests for multiple advertisers in parallel, we explore the possibility of collaboratively learning ad creative refinement via A/B tests of multiple advertisers. In particular, given an input ad creative, we study approaches to refine the given ad text and image by: (i) generating new ad text, (ii) recommending keyphrases for new ad text, and (iii) recommending image tags (objects in image) to select new ad image. Based on A/B tests conducted by multiple advertisers, we form pairwise examples of inferior and superior ad creatives, and use such pairs to train models for the above tasks. For generating new ad text, we demonstrate the efficacy of an encoder-decoder architecture with copy mechanism, which allows some words from the (inferior) input text to be copied to the output while incorporating new words associated with higher click-through-rate. For the keyphrase and image tag recommendation task, we demonstrate the efficacy of a deep relevance matching model, as well as the relative robustness of ranking approaches compared to ad text generation in cold-start scenarios with unseen advertisers. We also share broadly applicable insights from our experiments using data from the Yahoo Gemini ad platform.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
