  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.
