Please use this identifier to cite or link to this item:
Title: Learning to rank audience for behavioral targeting in display ads
Authors: Tang, J.
Liu, N.
Yan, J.
Shen, Y.
Guo, S.
Gao, B.
Yan, S. 
Zhang, M.
Keywords: behavioral targeting
display ads
user ranking
Issue Date: 2011
Citation: Tang, J.,Liu, N.,Yan, J.,Shen, Y.,Guo, S.,Gao, B.,Yan, S.,Zhang, M. (2011). Learning to rank audience for behavioral targeting in display ads. International Conference on Information and Knowledge Management, Proceedings : 605-610. ScholarBank@NUS Repository.
Abstract: Behavioral targeting (BT), which aims to sell advertisers those behaviorally related user segments to deliver their advertisements, is facing a bottleneck in serving the rapid growth of long tail advertisers. Due to the small business nature of the tail advertisers, they generally expect to accurately reach a small group of audience, which is hard to be satisfied by classical BT solutions with large size user segments. In this paper, we propose a novel probabilistic generative model named Rank Latent Dirichlet Allocation (RANKLDA) to rank audience according to their ads click probabilities for the long tail advertisers to deliver their ads. Based on the basic assumption that users who clicked the same group of ads will have a higher probability of sharing similar latent search topical interests, RANKLDA combines topic discovery from users' search behaviors and learning to rank users from their ads click behaviors together. In computation, the topic learning could be enhanced by the supervised information of the rank learning and simultaneously, the rank learning could be better optimized by considering the discovered topics as features. This co-optimization scheme enhances each other iteratively. Experiments over the real click-through log of display ads in a public ad network show that the proposed RANKLDA model can effectively rank the audience for the tail advertisers. © 2011 ACM.
Source Title: International Conference on Information and Knowledge Management, Proceedings
ISBN: 9781450307178
DOI: 10.1145/2063576.2063666
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Jan 29, 2023

Page view(s)

checked on Jan 26, 2023

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.