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Title: A multimodal approach for improving market price estimation in online advertising
Authors: Wang, Tengyun
Yang, Haizhi
Liu, Yang
Yu, Han
Song, Hengjie
Keywords: Engineering::Computer science and engineering
Issue Date: 2023
Source: Wang, T., Yang, H., Liu, Y., Yu, H. & Song, H. (2023). A multimodal approach for improving market price estimation in online advertising. Knowledge-Based Systems, 266, 110392-.
Project: AISG2-RP-2020-019
Journal: Knowledge-Based Systems
Abstract: Learning the distribution of market prices is an important and challenging issue for demand-side platforms (DSPs) that serve as advertisers’ agents to compete for online advertising placements in real-time bidding (RTB) systems. Many existing approaches make an assumption that the market prices follow an unimodal distribution. However, based on analytical insights from real-world datasets, we found the distinct multimodal characteristics underlying the distribution of market prices. Moreover, the impression-level features for each ad are also ignored by these approaches in prediction, reducing the accuracy further. To address these problems, a Gaussian Mixture Model (GMM) is proposed in this paper to describe and discriminate the multimodal distribution of market price by utilizing the impression-level features. To further improve its robustness, GMM is extended into a censored version (CGMM) to handle the right-censored challenge in RTB systems (i.e., the market price is only visible to the winner of the ad auction. Thus, the dataset is always biased). Extensive experiments on two real-world public datasets demonstrate that GMM and CGMM significantly outperform 10 state-of-the-art baselines in terms of Wasserstein distance, KL-divergence, ANLP and MSE. To the best of our knowledge, this paper is the first work to simultaneously deal with the multimodal nature of market price distribution and the right-censored challenge in existing RTB systems. It will enable future RTB systems to develop more realistic bidding strategies to enhance the efficiency of online advertising placement auctioning.
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2023.110392
Schools: School of Computer Science and Engineering 
Rights: © 2023 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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