TY - GEN
T1 - Audience expansion based on user browsing history
AU - Tziortziotis, Nikolaos
AU - Qiu, Yang
AU - Hue, Martial
AU - Vazirgiannis, Michalis
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/18
Y1 - 2021/7/18
N2 - A huge number of display advertising campaigns are launched every day by advertisers in order to promote their products or services. The main objective of each advertiser is to display ads to specific groups of users, i.e. users who meet specific criteria or their interests are related to the promoted products or services. Audience expansion, also known as audience look-alike targeting, is one of the major display advertising techniques that helps advertisers to discover audiences with similar attributes to a target audience who is interested in advertisers' products or services. In this paper, we present different audience expansion schemes able to identify users with similar browsing interests to those of the seed users provided by the advertiser. The proposed audience expansion schemes are based on different unsupervised representation models that are able to capture the interests of the users according to their browsing history. We have conducted an extensive empirical study on a real data collected from an advertising platform to analyse the effectiveness of the proposed schemes to expand the audiences of five different advertisers.
AB - A huge number of display advertising campaigns are launched every day by advertisers in order to promote their products or services. The main objective of each advertiser is to display ads to specific groups of users, i.e. users who meet specific criteria or their interests are related to the promoted products or services. Audience expansion, also known as audience look-alike targeting, is one of the major display advertising techniques that helps advertisers to discover audiences with similar attributes to a target audience who is interested in advertisers' products or services. In this paper, we present different audience expansion schemes able to identify users with similar browsing interests to those of the seed users provided by the advertiser. The proposed audience expansion schemes are based on different unsupervised representation models that are able to capture the interests of the users according to their browsing history. We have conducted an extensive empirical study on a real data collected from an advertising platform to analyse the effectiveness of the proposed schemes to expand the audiences of five different advertisers.
U2 - 10.1109/IJCNN52387.2021.9533392
DO - 10.1109/IJCNN52387.2021.9533392
M3 - Conference contribution
AN - SCOPUS:85116449651
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Joint Conference on Neural Networks, IJCNN 2021
Y2 - 18 July 2021 through 22 July 2021
ER -