Targeted display advertising: the case of preferential attachment

Abstract

An average adult is exposed to hundreds of digital advertisements daily 1 , 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 coldstart. We present an experimental analysis on the historical logs of a major display advertising platform 2 . 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.

A federated learning framework based on transfer learning and knowledge distillation for targeted advertising

“…Therefore, for the image classification model learned on the large dataset, a method is proposed, by transferring the pre-trained deep image classification to solve the scene object recognition problem in the specific task of the model of movies, TV dramas, short videos, and other TV programs, the experiment shows that the model is more effective and efficient ( Lian et al, 2019 ). Manchanda et al (2019) combine transfer learning and domain adaptation to use the similarity between users to transfer information from users with enough data to users without any active data, thus reducing time costs.…”

A Machine Learning Approach for Solving the Frozen User Cold-Start Problem in Personalized Mobile Advertising Systems

“…The new user-When there is a new user in the system that has not yet interacted with enough data objects (e.g., rated very few movies). Due to lack of data, the system is not able to generate an accurate model and, thus, it cannot provide adequate recommendations to him/her [6]. A special case of cold-start arises when there is no data at all available for the user (also referred as 'frozen start/user') [7,8].…”

“…When a new ad comes, the trained generator initializes the embedding of its ID by feeding its contents and attributes [17]. Manchanda et al, 2020 implemented two domain adaptation approaches (interpretable anchored domain adaptation-IADA, and latent anchored domain adaptation-LADA) that leverage the similarity among the partners to transfer information from the partners with sufficient data to similar partners with insufficient data [6].…”