Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting

Yi Feng, Ting Wang, Chuanyi Li, Vincent Ng, Jidong Ge, Bin Luo, Yucheng Hu, Xiaopeng Zhang


Abstract
User targeting is an essential task in the modern advertising industry: given a package of ads for a particular category of products (e.g., green tea), identify the online users to whom the ad package should be targeted. A (ad package specific) user targeting model is typically trained using historical clickthrough data: positive instances correspond to users who have clicked on an ad in the package before, whereas negative instances correspond to users who have not clicked on any ads in the package that were displayed to them. Collecting a sufficient amount of positive training data for training an accurate user targeting model, however, is by no means trivial. This paper focuses on the development of a method for automatic augmentation of the set of positive training instances. Experimental results on two datasets, including a real-world company dataset, demonstrate the effectiveness of our proposed method.
Anthology ID:
2021.findings-emnlp.129
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1493–1503
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.129
DOI:
10.18653/v1/2021.findings-emnlp.129
Bibkey:
Cite (ACL):
Yi Feng, Ting Wang, Chuanyi Li, Vincent Ng, Jidong Ge, Bin Luo, Yucheng Hu, and Xiaopeng Zhang. 2021. Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1493–1503, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting (Feng et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.129.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.129.mp4