research-article

We Know What You Want: An Advertising Strategy Recommender System for Online Advertising

Authors:

Zhizhuang Xing,

Xiaoqiang ZhuAuthors Info & Claims

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages 2919 - 2927

https://doi.org/10.1145/3447548.3467175

Published: 14 August 2021 Publication History

Abstract

Advertising expenditures have become the major source of revenue for e-commerce platforms. Providing good advertising experiences for advertisers by reducing their costs of trial and error in discovering the optimal advertising strategies is crucial for the long-term prosperity of online advertising. To achieve this goal, the advertising platform needs to identify the advertiser's optimization objectives, and then recommend the corresponding strategies to fulfill the objectives. In this work, we first deploy a prototype of strategy recommender system on Taobao display advertising platform, which indeed increases the advertisers' performance and the platform's revenue, indicating the effectiveness of strategy recommendation for online advertising. We further augment this prototype system by explicitly learning the advertisers' preferences over various advertising performance indicators and then optimization objectives through their adoptions of different recommending advertising strategies. We use contextual bandit algorithms to efficiently learn the advertisers' preferences and maximize the recommendation adoption, simultaneously. Simulation experiments based on Taobao online bidding data show that the designed algorithms can effectively optimize the strategy adoption rate of advertisers.

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Cited By

Su YXiang MChen YLi YQin TZhang HLi YLiu XBaeza-Yates RBonchi F(2024)Spending Programmed Bidding: Privacy-friendly Bid Optimization with ROI Constraint in Online AdvertisingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671540(5731-5740)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671540
De Biasio AJannach DNavarin N(2024)Model-based approaches to profit-aware recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123642249:PBOnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123642
De Biasio ANavarin NJannach D(2024)Economic recommender systems – a systematic reviewElectronic Commerce Research and Applications10.1016/j.elerap.2023.10135263:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.elerap.2023.101352
Show More Cited By

Index Terms

We Know What You Want: An Advertising Strategy Recommender System for Online Advertising
1. Applied computing
  1. Electronic commerce
2. Information systems
  1. World Wide Web
    1. Online advertising
      1. Display advertising

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Published In

cover image ACM Conferences

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

August 2021

4259 pages

ISBN:9781450383325

DOI:10.1145/3447548

General Chairs:
Feida Zhu
Singapore Management University
,
Beng Chin Ooi
National University of Singapore
,
Chunyan Miao
Nanyang Technology University
,
Program Chairs:
Haixun Wang,
Iryna Skrypnyk,
Wynne Hsu,
Sanjay Chawla

Copyright © 2021 ACM.

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Published: 14 August 2021

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Shanghai Science and Technology fund
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China NSF grant
Alibaba Group

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KDD '21

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KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2021

Virtual Event, Singapore

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Su YXiang MChen YLi YQin TZhang HLi YLiu XBaeza-Yates RBonchi F(2024)Spending Programmed Bidding: Privacy-friendly Bid Optimization with ROI Constraint in Online AdvertisingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671540(5731-5740)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671540
De Biasio AJannach DNavarin N(2024)Model-based approaches to profit-aware recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123642249:PBOnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123642
De Biasio ANavarin NJannach D(2024)Economic recommender systems – a systematic reviewElectronic Commerce Research and Applications10.1016/j.elerap.2023.10135263:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.elerap.2023.101352
Ou WChen BDai XZhang WLiu WTang RYu Y(2023)A Survey on Bid Optimization in Real-Time Bidding Display AdvertisingACM Transactions on Knowledge Discovery from Data10.1145/362860318:3(1-31)Online publication date: 9-Dec-2023
https://dl.acm.org/doi/10.1145/3628603
Zhang HNiu LZheng ZZhang ZGu SWu FYu CXu JChen GZheng BSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)A Personalized Automated Bidding Framework for Fairness-aware Online AdvertisingProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599765(5544-5553)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599765
Zhang YZhou JRen YZhang YYang XLi MShi QLi L(2023)ALT: An Automatic System for Long Tail Scenario Modeling2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00231(3017-3030)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00231
Benamara IViennet E(2023)Contextual Advertising Strategy Generation via Attention and Interaction Guidance2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302533(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/DSAA60987.2023.10302533

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