Abstract
A Novel Feature Engineering Framework in Digital Advertising Platform
Highlights
Digital advertising, which has only been around for two decades, is one of the most effective manners for all sizes companies and businesses to expand their reach, find new clients, and diversify their revenue streams
This paper proposes a novel feature engineering framework for ad event prediction in digital advertising platform which has been applied on big data
We introduce two new statistical measures which can be used for feature selection: i) the adjusted chi-squared test and ii) the adjusted mutual information
Summary
Digital advertising, which has only been around for two decades, is one of the most effective manners for all sizes companies and businesses to expand their reach, find new clients, and diversify their revenue streams. The event prediction is defined to estimate the ratio of events such as videos, clicks or conversions to impressions of advertisements that will be displayed. Ads are announcements online about something such as a product or service, and the principal components in a marketer’s paid advertising campaigns. They are sold on a ’Pay-Per-Click’ (PPC) basis or even ’Pay-Per-Acquisition’ (PPA), meaning the company only pays for ad clicks, conversions or any other pre-defined actions, not ad views. The Click-Through Rate (CTR) and the Conversion Rate (CVR) are very important indicators to measure the effectiveness of advertising display, and to maximize the expected value, one needs to predict
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