A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Automatically Mapping Ad Targeting Criteria between Online Ad Platforms
Tekijät: Salminen Joni, Jung Soon-Gyo, Jansen Bernard J.
Toimittaja: Bui Tung X.
Konferenssin vakiintunut nimi: Hawaii International Conference on System Sciences
Julkaisuvuosi: 2021
Kokoomateoksen nimi: The proceedings of the 54th Hawaii International Conference on System Sciences 2021
Aloitussivu: 940
Lopetussivu: 948
ISBN: 978-0-9981331-4-0
ISSN: 2572-6862
DOI: https://doi.org/10.24251/HICSS.2021.115
Verkko-osoite: http://hdl.handle.net/10125/70727
Rinnakkaistallenteen osoite: 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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
