A4 Refereed article in a conference publication
Automatically Mapping Ad Targeting Criteria between Online Ad Platforms
Authors: Salminen Joni, Jung Soon-Gyo, Jansen Bernard J.
Editors: Bui Tung X.
Conference name: Hawaii International Conference on System Sciences
Publication year: 2021
Book title : The proceedings of the 54th Hawaii International Conference on System Sciences 2021
First page : 940
Last page: 948
ISBN: 978-0-9981331-4-0
ISSN: 2572-6862
DOI: https://doi.org/10.24251/HICSS.2021.115
Web address : http://hdl.handle.net/10125/70727
Self-archived copy’s web address: https://research.utu.fi/converis/portal/Publication/50746281
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.
Downloadable publication This is an electronic reprint of the original article. |  |
