  An average adult is exposed to hundreds of digital advertisements daily
(https://www.mediadynamicsinc.com/uploads/files/PR092214-Note-only-150-Ads-2mk.pdf),
making the digital advertisement industry a classic example of a
big-data-driven platform. As such, the ad-tech industry relies on historical
engagement logs (clicks or purchases) to identify potentially interested users
for the advertisement campaign of a partner (a seller who wants to target users
for its products). The number of advertisements that are shown for a partner,
and hence the historical campaign data available for a partner depends upon the
budget constraints of the partner. Thus, enough data can be collected for the
high-budget partners to make accurate predictions, while this is not the case
with the low-budget partners. This skewed distribution of the data leads to
"preferential attachment" of the targeted display advertising platforms towards
the high-budget partners. In this paper, we develop "domain-adaptation"
approaches to address the challenge of predicting interested users for the
partners with insufficient data, i.e., the tail partners. Specifically, we
develop simple yet effective approaches that leverage the similarity among the
partners to transfer information from the partners with sufficient data to
cold-start partners, i.e., partners without any campaign data. Our approaches
readily adapt to the new campaign data by incremental fine-tuning, and hence
work at varying points of a campaign, and not just the cold-start. We present
an experimental analysis on the historical logs of a major display advertising
platform (https://www.criteo.com/). Specifically, we evaluate our approaches
across 149 partners, at varying points of their campaigns. Experimental results
show that the proposed approaches outperform the other "domain-adaptation"
approaches at different time points of the campaigns.
