  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 $k$, we are
interested in finding the optimal combination of conceptual nodes that maximize
the CTR. A discussion of the hardness and possible NP-hardness of the
optimization problem is provided. Then, we propose a greedy algorithm and a
genetic algorithm to find near-optimal combinations of conceptual nodes in
polynomial time, with the genetic algorithm nearly matching the optimal
solution. We observe that Decision Tree Regressor and Random Forest Regressor
exhibit the highest Pearson correlation coefficients w.r.t. click predictions
and real click values. Additionally, we find that the conceptual nodes of
"politics", "celebrity", and "organization" are notably more influential than
other considered conceptual nodes.
