JSAI2023

JSAI2023

Jun 6 - Jun 9, 2023Kumamoto-Jo Hall +online
The Japanese Society for Artificial Intelligence
JSAI2023

JSAI2023

Jun 6 - Jun 9, 2023Kumamoto-Jo Hall +online

[1U5-IS-2b-05]Predicting CTR of Responsive Search Ads Using Handcrafted Features

〇Melvin Charles Ortua Dy1(1. OPT, Inc.)
[[Online, Regular]]

Keywords:

Machine Learning,Feature Engineering,Advertising

In this paper, I demonstrate that a reasonably sized set of handcrafted features (866, applied to titles and description texts separately) plus encoded metadata can be used to predict the click-through rates of the dynamic Responsive Search Ad format, exceeding the performance of some fine-tuned Transformer-based large language models at a fraction of the training cost.