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research-article

Hierarchically Constrained Adaptive Ad Exposure in Feeds

Authors:

Bo ZhengAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 3003 - 3012

https://doi.org/10.1145/3511808.3557103

Published: 17 October 2022 Publication History

Abstract

A contemporary feed application usually provides blended results of organic items and sponsored items~(ads) to users. Conventionally, ads are exposed at fixed positions. Such a fixed ad exposure strategy is inefficient due to ignoring users' personalized preferences towards ads. To this end,adaptive ad exposure is becoming an appealing strategy to boost the overall performance of the feed. However, existing approaches to implement the adaptive ad exposure strategy suffer from several limitations: 1) they usually fall into sub-optimal solutions because of only focusing on request-level optimization without consideration of the application-level performance and constraints, 2) they neglect the necessity of keeping the game-theoretical properties of ad auctions, and 3) they can hardly be deployed in large-scale applications due to high computational complexity. In this paper, we focus on the application-level performance optimization under hierarchical constraints in feeds and formulate adaptive ad exposure as a Dynamic Knapsack Problem. We propose Hierarchically Constrained Adaptive Ad Exposure~(HCA2E) that possesses the desirable game-theoretical properties, computational efficiency, and performance robustness. Comprehensive offline and online experiments on a leading e-commerce application demonstrate the performance superiority of HCA2E.

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Cited By

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
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
Shi XYang FWang ZWu XGuan MLiao GYongkang WWang XWang DSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerceProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599886(4823-4831)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599886
Show More Cited By

Index Terms

Hierarchically Constrained Adaptive Ad Exposure in Feeds
1. Information systems
  1. Information systems applications
    1. Computational advertising

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

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Published: 17 October 2022

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Science and Technology Innovation 2030 ''New Generation Artificial Intelligence" Major Project
China NSF grant
Shanghai Science and Technology fund

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CIKM '22

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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

GA, Atlanta, USA

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

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Cited By

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
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
Shi XYang FWang ZWu XGuan MLiao GYongkang WWang XWang DSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerceProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599886(4823-4831)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599886
Kong LWang LZhao XJin JLin ZHu JShao JChen HDuh WHuang HKato MMothe JPoblete B(2023)LOVF: Layered Organic View Fusion for Click-through Rate Prediction in Online AdvertisingProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592014(2139-2143)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592014

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