Smart Advertisement for Maximal Clicks in Online Social Networks Without User Data
Nathaniel Hudson, Hana Khamfroush, Brent Harrison, Adam Craig

TL;DR
This paper introduces a user-data-independent method for maximizing ad CTR in social networks by using NLP to extract concepts and applying optimization algorithms to select the best combination of these concepts, achieving near-optimal results.
Contribution
It presents a novel approach that predicts CTR without user data by leveraging NLP-extracted concepts and formulates the selection as an optimization problem solved by greedy and genetic algorithms.
Findings
Decision Tree and Random Forest regressors perform best in CTR prediction.
The optimization problem is likely NP-hard, but near-optimal solutions are achievable.
Concepts like politics, celebrity, and organization are highly influential.
Abstract
Click-through rate (CTR) prediction of advertisements on online social network platforms to optimize advertising is of much interest. Prior works build machine learning models that take a user-centric approach in terms of training -- using predominantly user data to classify whether a user will click on an advertisement or not. While this approach has proven effective, it is inaccessible to most entities and relies heavily on user data. To accommodate for this, we first consider a large set of advertisement data on Facebook and use natural language processing (NLP) to extract key concepts that we call conceptual nodes. To predict the value of CTR for a combination of conceptual nodes, we use the advertisement data to train four machine learning (ML) models. We then cast the problem of finding the optimal combination of conceptual nodes as an optimization problem. Given a certain budget…
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