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An Adaptive Unified Allocation Framework for Guaranteed Display Advertising

Published: 15 February 2022 Publication History

Abstract

Guaranteed Display (GD) is widely used in e-commerce marketing for advertisers to acquire an agreed-upon number of impressions with target audiences. With the main objective to maximize the contract delivery rate under contract constraints, user interest (such as click-through rate and conversion rate) is also essential to improve the long-time return on investment for advertisers and the e-commerce platform. In this paper, we design an adaptive unified allocation framework (AUAF) by not only considering supply of audience impressions in request-level but also avoiding over-allocation of audience impressions. Specifically, our allocation model simultaneously optimizes the contract delivery and the match between advertisements and user interests with explicit constraint to prevent unnecessary allocation. Facing the challenge of serving billion-scale requests per day, a parameter-server based parallel optimization algorithm is also developed, enabling the proposed allocation model to be efficiently optimized and incrementally updated in minutes. Thus, the offline optimization results and the online decisions can be synchronized for real-time serving. In other words, our approach can achieve adaptive pacing that is consistent with the optimal allocation solution. Our extensive experimental results demonstrate that the proposed AUAF framework can improve both contract delivery rate and average click-through rate (CTR), which we use to measure the user interest in this paper. The improvements on CTR are statistically significant in comparison with existing methods. Moreover, since March 2020, AUAF has been deployed in the guaranteed display advertising system of Alibaba, bringing more than 10% increase on CTR without loss of contract delivery rate, which has resulted in significant value creation for the business.

Supplementary Material

MP4 File (10.1145:3488560.3498500-WSDM22-fp688.mp4)
We present an adaptive unified allocation framework (AUAF) for guaranteed display advertising. First we propose an over-allocation preventing model to maximizing delivery rate and user interest simultaneously, with consideration of both supply and demand resource constraints. We develop efficient dual-based parallel algorithm to solve the optimal allocation model, which can support online serving and adaptive pacing of the advertising system. Extensive experiments and online A/B testing show the advantages of our framework in improving both delivery rate and user interest. AUAF has been deployed in Alibaba?s GD advertising system for more than one year and has brought considerable revenue improvement.

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Cited By

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  • (2024)Bi-Objective Contract Allocation for Guaranteed Delivery AdvertisingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671752(1691-1700)Online publication date: 25-Aug-2024
  • (2024)Follow the LIBRA: Guiding Fair Policy for Unified Impression Allocation via Adversarial RewardingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635756(750-759)Online publication date: 4-Mar-2024
  • (2024)ROI constrained optimal online allocation in sponsored searchScientific Reports10.1038/s41598-024-77506-314:1Online publication date: 29-Oct-2024
  • Show More Cited By

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      cover image ACM Conferences
      WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
      February 2022
      1690 pages
      ISBN:9781450391320
      DOI:10.1145/3488560
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 15 February 2022

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      Author Tags

      1. computational advertising
      2. e-commerce marketing
      3. guaranteed display advertising
      4. optimal allocation

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      View all
      • (2024)Bi-Objective Contract Allocation for Guaranteed Delivery AdvertisingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671752(1691-1700)Online publication date: 25-Aug-2024
      • (2024)Follow the LIBRA: Guiding Fair Policy for Unified Impression Allocation via Adversarial RewardingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635756(750-759)Online publication date: 4-Mar-2024
      • (2024)ROI constrained optimal online allocation in sponsored searchScientific Reports10.1038/s41598-024-77506-314:1Online publication date: 29-Oct-2024
      • (2023)End-to-End Inventory Prediction and Contract Allocation for Guaranteed Delivery AdvertisingProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599332(1677-1686)Online publication date: 6-Aug-2023

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