research-article

Ads Allocation in Feed via Constrained Optimization

Authors:

Birjodh Tiwana,

Shaunak ChatterjeeAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 3386 - 3394

https://doi.org/10.1145/3394486.3403391

Published: 20 August 2020 Publication History

Abstract

Social networks and content publishing platforms have newsfeed applications, which show both organic content to drive engagement, and ads to drive revenue. This paper focuses on the problem of ads allocation in a newsfeed to achieve an optimal balance of revenue and engagement. To the best of our knowledge, we are the first to report practical solutions to this business-critical and popular problem in industry.

The paper describes how large-scale recommender system like feed ranking works, and why it is useful to consider ads allocation as a post-operation once the ranking of organic items and (separately) the ranking of ads are done. A set of computationally lightweight algorithms are proposed based on various sets of assumptions in the context of ads on the LinkedIn newsfeed. Through both offline simulation and online A/B tests, benefits of the proposed solutions are demonstrated. The best performing algorithm is currently fully deployed on the LinkedIn newsfeed and is serving all live traffic.

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Cited By

Shi WFu CXu QChen SZhang JZhu QHua ZYang SSerra ESpezzano F(2024)Ads Supply Personalization via Doubly Robust LearningProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680035(4874-4881)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680035
Li XWang ZZhu BHe FWang YWang XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in FeedProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657774(1211-1220)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657774
Zhang ZZhang QWu XShi XLiao GWang YWang XZhao DChua TNgo CKumar RLauw HKa-Wei Lee R(2024)User Response Modeling in Reinforcement Learning for Ads AllocationCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648310(131-140)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648310
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Index Terms

Ads Allocation in Feed via Constrained Optimization
1. Information systems

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Published In

cover image ACM Conferences

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

General Chairs:
Rajesh Gupta
UC San Diego, USA
,
Yan Liu
USC, USA
,
Program Chairs:
Mohak Shah
LG Electronics, USA
,
Suju Rajan
Linkedin, USA
,
Publications Chairs:
Jiliang Tang
Michigan State, USA
,
B. Aditya Prakash
Georgia Tech, USA

Copyright © 2020 ACM.

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Publication History

Published: 20 August 2020

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KDD '20

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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

July 6 - 10, 2020

CA, Virtual Event, USA

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

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Cited By

Shi WFu CXu QChen SZhang JZhu QHua ZYang SSerra ESpezzano F(2024)Ads Supply Personalization via Doubly Robust LearningProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680035(4874-4881)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680035
Li XWang ZZhu BHe FWang YWang XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in FeedProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657774(1211-1220)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657774
Zhang ZZhang QWu XShi XLiao GWang YWang XZhao DChua TNgo CKumar RLauw HKa-Wei Lee R(2024)User Response Modeling in Reinforcement Learning for Ads AllocationCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648310(131-140)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648310
Li NMa YZhao YWang QZhang ZYu CXu JZheng BDeng XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Ad vs Organic: Revisiting Incentive Compatible Mechanism Design in E-commerce PlatformsProceedings of the ACM Web Conference 202410.1145/3589334.3645638(235-244)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645638
Liu YLiu WXia WZhu JZhang WDong ZWang YTang RZhang RYu Y(2024)Multi-sourced Integrated Ranking with Exposure FairnessAdvances in Knowledge Discovery and Data Mining10.1007/978-981-97-2262-4_17(207-218)Online publication date: 25-Apr-2024
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Liu YChen DZheng ZZhang ZYu CWu FChen GChua TLauw HSi LTerzi ETsaparas P(2023)Boosting Advertising Space: Designing Ad Auctions for Augment AdvertisingProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570381(1066-1074)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570381
Li WQi QWang CYu C(2023)Optimally integrating ad auction into e-commerce platformsTheoretical Computer Science10.1016/j.tcs.2023.114141976(114141)Online publication date: Oct-2023
https://doi.org/10.1016/j.tcs.2023.114141
Wang JZhao MXia WDong ZTang RZhang RHao JChen GHeng P(2023)RLMixer: A Reinforcement Learning Approach for Integrated Ranking with Contrastive User Preference ModelingAdvances in Knowledge Discovery and Data Mining10.1007/978-3-031-33380-4_31(400-413)Online publication date: 27-May-2023
https://doi.org/10.1007/978-3-031-33380-4_31
Wang ZLiao GShi XWu XZhang CZhu BWang YWang XWang DAl Hasan MXiong L(2022)Hybrid Transfer in Deep Reinforcement Learning for Ads AllocationProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557611(4560-4564)Online publication date: 17-Oct-2022
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Huang JTang XWang ZJia SBai YLiu ZCheng JLei JZhang YAl Hasan MXiong L(2022)Deep Presentation Bias Integrated Framework for CTR PredictionProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557579(4049-4053)Online publication date: 17-Oct-2022
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