@inproceedings{73c9d2f7888a469da76fe13d50c0af63,
title = "Automatically mapping ad targeting criteria between online Ad platforms",
abstract = "Targeting criteria in online advertising differ across platforms and frequently change. Because advertisers are increasingly taking a multi-channel approach to online marketing, there is a need to automatically map the targeting criteria between ad platforms. In this research, we test two algorithmic approaches - Word2Vec and WordNet - for mapping ad targeting criteria between Google Ads and Facebook Ads. The results show that Word2Vec outperforms WordNet in finding matches (97.5% vs. 63.6%), covering different criteria (20.0% vs. 13.5%), and having higher similarity scores. However, WordNet outperforms Word2Vec in expert evaluation (Mean Opinion Score = 3.05 vs. 2.46), implying that algorithmic performance metrics may not correlate with expert ratings. Overall, due to specific requirements for mapping ad targeting criteria, automatic means do not (at least yet) offer a satisfactory solution for replacing human judgment.",
author = "Joni Salminen and Jung, {Soon Gyo} and Jansen, {Bernard J.}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE Computer Society. All rights reserved.; 54th Annual Hawaii International Conference on System Sciences, HICSS 2021 ; Conference date: 04-01-2021 Through 08-01-2021",
year = "2021",
language = "English",
series = "Proceedings of the Annual Hawaii International Conference on System Sciences",
publisher = "IEEE Computer Society",
pages = "940--948",
editor = "Bui, {Tung X.}",
booktitle = "Proceedings of the 54th Annual Hawaii International Conference on System Sciences, HICSS 2021",
address = "United States",
}