2 research outputs found
Targeted display advertising: the case of preferential attachment
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.Comment: IEEE BigData 2019 pape
Distant-Supervised Slot-Filling for E-Commerce Queries
Slot-filling refers to the task of annotating individual terms in a query
with the corresponding intended product characteristics (product type, brand,
gender, size, color, etc.). These characteristics can then be used by a search
engine to return results that better match the query's product intent.
Traditional methods for slot-filling require the availability of training data
with ground truth slot-annotation information. However, generating such labeled
data, especially in e-commerce is expensive and time-consuming because the
number of slots increases as new products are added. In this paper, we present
distant-supervised probabilistic generative models, that require no manual
annotation. The proposed approaches leverage the readily available historical
query logs and the purchases that these queries led to, and also exploit
co-occurrence information among the slots in order to identify intended product
characteristics. We evaluate our approaches by considering how they affect
retrieval performance, as well as how well they classify the slots. In terms of
retrieval, our approaches achieve better ranking performance (up to 156%) over
Okapi BM25. Moreover, our approach that leverages co-occurrence information
leads to better performance than the one that does not on both the retrieval
and slot classification tasks