LargeScale Personalized Delivery for Guaranteed Display Advertising with RealTime Pacing Guaranteed display (GD) has been a successful model for display advertising. Existing solutions usually model GD services as a crowdlevel supply allocation problem. This formulation, however, not only ignores user heterogeneity within crowds, but also makes it difficult to incorporate individuallevel constraints. In this paper, we present an largescale system for personalized delivery in GD advertising services. A unique contribution is to model the allocation problem at the individual level that accounts for userad interactions. Therefore, our system can conveniently incorporate complex constraints, such as the priority of GD contracts, the display frequency of ads, and the effectiveness of ad slots arrangement. Moreover, we develop a realtime pacing strategy to fulfill GD contracts with smooth ad delivery and optimized ad performance, such as costperclick (CPC) and costperaction (CPA). Our system can be parallelized to efficiently compute the delivery solution with billions decision variables. Using both offline evaluation and online AB tests, we demonstrate that our solution is effective in terms of both accuracy and efficiency.

Targeted display advertising: the case of preferential attachment An average adult is exposed to hundreds of digital advertisements daily (this https URL), making the digital advertisement industry a classic example of a bigdatadriven platform. As such, the adtech 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 highbudget partners to make accurate predictions, while this is not the case with the lowbudget partners. This skewed distribution of the data leads to preferential attachment of the targeted display advertising platforms towards the highbudget partners. In this paper, we develop domainadaptation 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 coldstart partners, i.e., partners without any campaign data. Our approaches readily adapt to the new campaign data by incremental finetuning, and hence work at varying points of a campaign, and not just the coldstart. We present an experimental analysis on the historical logs of a major display advertising platform (this https URL). Specifically, we evaluate our approaches across 149 partners, at varying points of their campaigns. Experimental results show that the proposed approaches outperform the other domainadaptation approaches at different time points of the campaigns.