research-article

Automated Creative Optimization for E-Commerce Advertising

Authors:

Zhiqiang Zhang,

Kai ZhengAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 2304 - 2313

https://doi.org/10.1145/3442381.3449909

Published: 03 June 2021 Publication History

Abstract

Advertising creatives are ubiquitous in E-commerce advertisements and aesthetic creatives may improve the click-through rate (CTR) of the products. Nowadays smart advertisement platforms provide the function of compositing creatives based on source materials provided by advertisers. Since a great number of creatives can be generated, it is difficult to accurately predict their CTR given a limited amount of feedback. Factorization machine (FM), which models inner product interaction between features, can be applied for the CTR prediction of creatives. However, interactions between creative elements may be more complex than the inner product, and the FM-estimated CTR may be of high variance due to limited feedback. To address these two issues, we propose an Automated Creative Optimization (AutoCO) framework to model complex interaction between creative elements and to balance between exploration and exploitation. Specifically, motivated by AutoML, we propose one-shot search algorithms for searching effective interaction functions between elements. We then develop stochastic variational inference to estimate the posterior distribution of parameters based on the reparameterization trick, and apply Thompson Sampling for efficiently exploring potentially better creatives. We evaluate the proposed method with both a synthetic dataset and two public datasets. The experimental results show our method can outperform competing baselines with respect to cumulative regret. The online A/B test shows our method leads to a 7% increase in CTR compared to the baseline.

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Wu CLian DGe YZhou MChen E(2024)Attacking Social Media via Behavior PoisoningACM Transactions on Knowledge Discovery from Data10.1145/365467318:7(1-27)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3654673
Wu CLian DGe YZhou MChen ETao D(2024)Boosting Factorization Machines via Saliency-Guided MixupIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.335491046:6(4443-4459)Online publication date: Jun-2024
https://doi.org/10.1109/TPAMI.2024.3354910
Li GYang X(2024)Two-Stage Dynamic Creative Optimization Under Sparse Ambiguous Samples for e-Commerce AdvertisingSN Computer Science10.1007/s42979-024-03332-z5:8Online publication date: 26-Oct-2024
https://doi.org/10.1007/s42979-024-03332-z
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cover image ACM Conferences

WWW '21: Proceedings of the Web Conference 2021

April 2021

4054 pages

ISBN:9781450383127

DOI:10.1145/3442381

Editors:
Jure Leskovec
Stanford
,
Marko Grobelnik
Jožef Stefan Institute
,
Marc Najork
Google
,
Jie Tang
Tsinghua University
,
Leila Zia
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Published: 03 June 2021

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Cited By

Wu CLian DGe YZhou MChen E(2024)Attacking Social Media via Behavior PoisoningACM Transactions on Knowledge Discovery from Data10.1145/365467318:7(1-27)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3654673
Wu CLian DGe YZhou MChen ETao D(2024)Boosting Factorization Machines via Saliency-Guided MixupIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.335491046:6(4443-4459)Online publication date: Jun-2024
https://doi.org/10.1109/TPAMI.2024.3354910
Li GYang X(2024)Two-Stage Dynamic Creative Optimization Under Sparse Ambiguous Samples for e-Commerce AdvertisingSN Computer Science10.1007/s42979-024-03332-z5:8Online publication date: 26-Oct-2024
https://doi.org/10.1007/s42979-024-03332-z
Du ZFeng WWang HLi YWang JLi JZhang ZLv JZhu XJin JShen JLin ZShao J(2024)Towards Reliable Advertising Image Generation Using Human FeedbackComputer Vision – ECCV 202410.1007/978-3-031-72661-3_23(399-415)Online publication date: 27-Nov-2024
https://doi.org/10.1007/978-3-031-72661-3_23
Jiang LLi CChen HGao XZhong XQiu YYe SNiu DSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)AdSEE: Investigating the Impact of Image Style Editing on Advertisement AttractivenessProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599770(4239-4251)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599770
Tao WLiu HLi XDai QWen HChen ZChen HDuh WHuang HKato MMothe JPoblete B(2023)Event-Aware Adaptive Clustering Uplift Network for Insurance Creative RankingProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591980(1966-1970)Online publication date: 19-Jul-2023
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Wei PLiu SYang XWang LZheng BAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive DecodingProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531909(2634-2638)Online publication date: 6-Jul-2022
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Li GYang X(2022)Smartbanner: intelligent banner design framework that strikes a balance between creative freedom and design rulesMultimedia Tools and Applications10.1007/s11042-022-14138-782:12(18653-18667)Online publication date: 23-Nov-2022
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Nichifor ELixăndroiu RChițu IBrătucu GSumedrea SMaican CTecău A(2021)Eye Tracking and an A/B Split Test for Social Media Marketing Optimisation: The Connection between the User Profile and Ad Creative ComponentsJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1606012816:6(2319-2340)Online publication date: 11-Sep-2021
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