research-article

Domain adaptation in display advertising: an application for partner cold-start

Authors:

Karan Aggarwal,

S. Sathiya KeerthiAuthors Info & Claims

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

Pages 178 - 186

https://doi.org/10.1145/3298689.3347004

Published: 10 September 2019 Publication History

Abstract

Digital advertisements connects partners (sellers) to potentially interested online users. Within the digital advertisement domain, there are multiple platforms, e.g., user re-targeting and prospecting. Partners usually start with re-targeting campaigns and later employ prospecting campaigns to reach out to untapped customer base. There are two major challenges involved with prospecting. The first challenge is successful on-boarding of a new partner on the prospecting platform, referred to as partner cold-start problem. The second challenge revolves around the ability to leverage large amounts of re-targeting data for partner cold-start problem.

In this work, we study domain adaptation for the partner cold-start problem. To this end, we propose two domain adaptation techniques, SDA-DANN and SDA-Ranking. SDA-DANN and SDA-Ranking extend domain adaptation techniques for partner cold-start by incorporating sub-domain similarities (product category level information). Through rigorous experiments, we demonstrate that our method SDA-DANN outperforms baseline domain adaptation techniques on real-world dataset, obtained from a major online advertiser. Furthermore, we show that our proposed technique SDA-Ranking outperforms baseline methods for low CTR partners.

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Cited By

Häglund EBjörklund J(2024)AI-Driven Contextual Advertising: Toward Relevant Messaging Without Personal DataJournal of Current Issues & Research in Advertising10.1080/10641734.2024.233493945:3(301-319)Online publication date: 29-Apr-2024
https://doi.org/10.1080/10641734.2024.2334939
Shen YZhao LCheng WZhang ZZhou WKangyi L(2023)RESUS: Warm-up Cold Users via Meta-learning Residual User Preferences in CTR PredictionACM Transactions on Information Systems10.1145/356428341:3(1-26)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3564283
Chang ZLi MWang QLi SWang J(2023)Cross-Domain Requirements Linking via Adversarial-based Domain Adaptation2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)10.1109/ICSE48619.2023.00138(1596-1608)Online publication date: May-2023
https://doi.org/10.1109/ICSE48619.2023.00138
Show More Cited By

Index Terms

Domain adaptation in display advertising: an application for partner cold-start
1. Information systems
  1. World Wide Web
    1. Online advertising
      1. Display advertising

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cover image ACM Other conferences

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

September 2019

635 pages

ISBN:9781450362436

DOI:10.1145/3298689

General Chairs:
Toine Bogers
Aalborg University Copenhagen, Denmark
,
Alan Said
University of Gothenburg, Sweden
,
Program Chairs:
Peter Brusilovsky
University of Pittsburgh
,
Domonkos Tikk
Gravity R&D, Hungary

Copyright © 2019 ACM.

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Published: 10 September 2019

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RecSys '19

RecSys '19: Thirteenth ACM Conference on Recommender Systems

September 16 - 20, 2019

Copenhagen, Denmark

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RecSys '19 Paper Acceptance Rate 36 of 189 submissions, 19%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Häglund EBjörklund J(2024)AI-Driven Contextual Advertising: Toward Relevant Messaging Without Personal DataJournal of Current Issues & Research in Advertising10.1080/10641734.2024.233493945:3(301-319)Online publication date: 29-Apr-2024
https://doi.org/10.1080/10641734.2024.2334939
Shen YZhao LCheng WZhang ZZhou WKangyi L(2023)RESUS: Warm-up Cold Users via Meta-learning Residual User Preferences in CTR PredictionACM Transactions on Information Systems10.1145/356428341:3(1-26)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3564283
Chang ZLi MWang QLi SWang J(2023)Cross-Domain Requirements Linking via Adversarial-based Domain Adaptation2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)10.1109/ICSE48619.2023.00138(1596-1608)Online publication date: May-2023
https://doi.org/10.1109/ICSE48619.2023.00138
Bruun SLeśniak KBiasini MCarmignani VFilianos PLioma CMaistro M(2023)Graph-Based Recommendation for Sparse and Heterogeneous User InteractionsAdvances in Information Retrieval10.1007/978-3-031-28244-7_12(182-199)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-28244-7_12
Viktoratos ITsadiras A(2022)A Machine Learning Approach for Solving the Frozen User Cold-Start Problem in Personalized Mobile Advertising SystemsAlgorithms10.3390/a1503007215:3(72)Online publication date: 22-Feb-2022
https://doi.org/10.3390/a15030072
Li HChen XWang JWu DLiu X(2021)DAFIProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34949545:4(1-21)Online publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1145/3494954
Kirchmeyer MGallinari PRakotomamonjy AMantrach A(2021)Unsupervised domain adaptation with non-stochastic missing dataData Mining and Knowledge Discovery10.1007/s10618-021-00775-335:6(2714-2755)Online publication date: 12-Oct-2021
https://dl.acm.org/doi/10.1007/s10618-021-00775-3
Capelo MAggarwal KYadav P(2019)Combining Text and Image data for Product Recommendability Modeling2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006197(5992-5994)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006197
Manchanda SYadav PDoan KKeerthi S(2019)Targeted display advertising: the case of preferential attachment2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006184(1868-1877)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006184

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