In this article, we present a general approach to personalizing ads through encoding and learning from variable-length sequences of recent user actions and diverse representations. To this end we introduce a three-component module called the adSformer diversifiable personalization module (ADPM) that learns a dynamic user representation. We illustrate the module's effectiveness and flexibility by personalizing the Click-Through Rate (CTR) and Post-Click Conversion Rate (PCCVR) models used in sponsored search. The first component of the ADPM, the adSformer encoder, includes a novel adSformer block which learns the most salient sequence signals. ADPM's second component enriches the learned signal through visual, multimodal, and other pretrained representations. Lastly, the third ADPM "learned on the fly" component further diversifies the signal encoded in the dynamic user representation. The ADPM-personalized CTR and PCCVR models, henceforth referred to as adSformer CTR and adSformer PCCVR, outperform the CTR and PCCVR production baselines by $+2.66\%$ and $+2.42\%$, respectively, in offline Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Following the robust online gains in A/B tests, Etsy Ads deployed the ADPM-personalized sponsored search system to $100\%$ of traffic as of February 2023.

翻译：本文提出了一种通用的广告个性化方法，该方法通过对用户近期行为的可变长度序列及多样化表征进行编码与学习来实现。为此，我们引入了一个三组件模块——adSformer可多样化个性化模块（ADPM），该模块可学习动态用户表征。通过将所提模块应用于赞助搜索中的点击率（CTR）与点击后转化率（PCCVR）模型的个性化任务，我们验证了其有效性与灵活性。ADPM的第一个组件——adSformer编码器包含一个新颖的adSformer块，用于学习最显著的序列信号；第二个组件通过视觉、多模态及其他预训练表征对学习信号进行增强；第三个"即时学习"组件进一步多样化动态用户表征中编码的信号。基于ADPM个性化的CTR与PCCVR模型（以下简称adSformer CTR与adSformer PCCVR）在离线ROC-AUC指标上分别比生产基线CTR与PCCVR模型提升+2.66%与+2.42%。经过A/B测试的稳定在线增益验证，Etsy广告已于2023年2月将基于ADPM个性化的赞助搜索系统部署至100%流量中。