Optimal Auction Design in the Joint Advertising

• URL: http://arxiv.org/abs/2507.07418v1
• Date: Thu, 10 Jul 2025 04:21:01 GMT
• Title: Optimal Auction Design in the Joint Advertising
• Authors: Yang Li, Yuchao Ma, Qi Qi,
• Abstract summary: This paper identifies an optimal mechanism for joint advertising in a single-slot setting.<n>For multi-slot joint advertising, we propose textbfBundleNet, a novel bundle-based neural network approach specifically designed for joint advertising.
• Score: 6.338459630458534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online advertising is a vital revenue source for major internet platforms. Recently, joint advertising, which assigns a bundle of two advertisers in an ad slot instead of allocating a single advertiser, has emerged as an effective method for enhancing allocation efficiency and revenue. However, existing mechanisms for joint advertising fail to realize the optimality, as they tend to focus on individual advertisers and overlook bundle structures. This paper identifies an optimal mechanism for joint advertising in a single-slot setting. For multi-slot joint advertising, we propose \textbf{BundleNet}, a novel bundle-based neural network approach specifically designed for joint advertising. Our extensive experiments demonstrate that the mechanisms generated by \textbf{BundleNet} approximate the theoretical analysis results in the single-slot setting and achieve state-of-the-art performance in the multi-slot setting. This significantly increases platform revenue while ensuring approximate dominant strategy incentive compatibility and individual rationality.

Related papers

• Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems [54.709976343045824]
  Current offline reinforcement learning (RL) methods face substantial challenges when applied to sparse advertising scenarios.<n>We propose MTORL, a novel multi-task offline RL model that targets two key objectives.<n>We employ multi-task learning to decode actions and rewards, simultaneously addressing channel recommendation and budget allocation.
  arXiv  Detail & Related papers  (2025-06-29T05:05:13Z)
• EGA-V2: An End-to-end Generative Framework for Industrial Advertising [19.927005856735445]
  We introduce End-to-End Generative Advertising (EGA-V2), the first unified framework that holistically models user interests, point-of-interest (POI) and creative generation, ad allocation, and payment optimization.<n>Our results highlight its potential as a pioneering fully generative advertising solution, paving the way for next-generation industrial ad systems.
  arXiv  Detail & Related papers  (2025-05-23T06:55:02Z)
• Fair Allocation in Dynamic Mechanism Design [57.66441610380448]
  We consider a problem where an auctioneer sells an indivisible good to groups of buyers in every round, for a total of $T$ rounds. The auctioneer aims to maximize their discounted overall revenue while adhering to a fairness constraint that guarantees a minimum average allocation for each group.
  arXiv  Detail & Related papers  (2024-05-31T19:26:05Z)
• Optimizing Search Advertising Strategies: Integrating Reinforcement Learning with Generalized Second-Price Auctions for Enhanced Ad Ranking and Bidding [36.74368014856906]
  We propose a model that adjusts to varying user interactions and optimize the balance between advertiser cost, user relevance, and platform revenue. Our results suggest significant improvements in ad placement accuracy and cost efficiency, demonstrating the model's applicability in real-world scenarios.
  arXiv  Detail & Related papers  (2024-05-22T06:30:55Z)
• VFed-SSD: Towards Practical Vertical Federated Advertising [53.08038962443853]
  We propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. Specifically, we develop a self-supervised task MatchedPair Detection (MPD) to exploit the vertically partitioned unlabeled data. Our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.
  arXiv  Detail & Related papers  (2022-05-31T17:45:30Z)
• Bidding Agent Design in the LinkedIn Ad Marketplace [16.815498720115443]
  We establish a general optimization framework for the design of automated bidding agent in online marketplaces. As a result, the framework allows, for instance, the joint optimization of a group of ads across multiple platforms each running its own auction format. We share practical learnings of the deployed bidding system in the LinkedIn ad marketplace based on this framework.
  arXiv  Detail & Related papers  (2022-02-25T03:01:57Z)
• A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising [53.636153252400945]
  We propose a general Multi-Agent reinforcement learning framework for Auto-Bidding, namely MAAB, to learn the auto-bidding strategies. Our approach outperforms several baseline methods in terms of social welfare and guarantees the ad platform's revenue.
  arXiv  Detail & Related papers  (2021-06-11T08:07:14Z)
• A novel auction system for selecting advertisements in Real-Time bidding [68.8204255655161]
  Real-Time Bidding is a new Internet advertising system that has become very popular in recent years. We propose an alternative betting system with a new approach that not only considers the economic aspect but also other relevant factors for the functioning of the advertising system.
  arXiv  Detail & Related papers  (2020-10-22T18:36:41Z)
• Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising [52.3825928886714]
  We formulate the sequential advertising strategy optimization as a dynamic knapsack problem. We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space. To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach.
  arXiv  Detail & Related papers  (2020-06-29T18:50:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.