Multi-Platform Budget Management in Ad Markets with Non-IC Auctions

• URL: http://arxiv.org/abs/2306.07352v1
• Date: Mon, 12 Jun 2023 18:21:10 GMT
• Title: Multi-Platform Budget Management in Ad Markets with Non-IC Auctions
• Authors: Fransisca Susan, Negin Golrezaei, Okke Schrijvers
• Abstract summary: In online advertising markets, budget-constrained advertisers acquire ad placements through repeated bidding in auctions on various platforms. We present a strategy for bidding optimally in a set of auctions that may or may not be incentive-compatible under the presence of budget constraints. Our strategy maximizes the expected total utility across auctions while satisfying the advertiser's budget constraints in expectation.
• Score: 6.037383467521294
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In online advertising markets, budget-constrained advertisers acquire ad placements through repeated bidding in auctions on various platforms. We present a strategy for bidding optimally in a set of auctions that may or may not be incentive-compatible under the presence of budget constraints. Our strategy maximizes the expected total utility across auctions while satisfying the advertiser's budget constraints in expectation. Additionally, we investigate the online setting where the advertiser must submit bids across platforms while learning about other bidders' bids over time. Our algorithm has $O(T^{3/4})$ regret under the full-information setting. Finally, we demonstrate that our algorithms have superior cumulative regret on both synthetic and real-world datasets of ad placement auctions, compared to existing adaptive pacing algorithms.

Related papers

• Procurement Auctions via Approximately Optimal Submodular Optimization [53.93943270902349]
  We study procurement auctions, where an auctioneer seeks to acquire services from strategic sellers with private costs. Our goal is to design computationally efficient auctions that maximize the difference between the quality of the acquired services and the total cost of the sellers.
  arXiv  Detail & Related papers  (2024-11-20T18:06:55Z)
• Online Ad Procurement in Non-stationary Autobidding Worlds [10.871587311621974]
  We introduce a primal-dual algorithm for online decision making with multi-dimension decision variables, bandit feedback and long-term uncertain constraints. We show that our algorithm achieves low regret in many worlds when procurement outcomes are generated through procedures that are adversarial, adversarially corrupted, periodic, and ergodic.
  arXiv  Detail & Related papers  (2023-07-10T00:41:08Z)
• Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets [17.937079224726073]
  We study coordinated online bidding algorithms in repeated second-price auctions with budgets. We propose algorithms that guarantee every client a higher utility than the best she can get under independent bidding.
  arXiv  Detail & Related papers  (2023-06-13T11:55:04Z)
• Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics [53.62091043347035]
  We study a game between autobidding algorithms that compete in an online advertising platform. We propose a gradient-based learning algorithm that is guaranteed to satisfy all constraints and achieves vanishing individual regret.
  arXiv  Detail & Related papers  (2023-01-30T21:59:30Z)
• No-regret Learning in Repeated First-Price Auctions with Budget Constraints [5.834615090865286]
  We propose an RL-based bidding algorithm against the optimal non-anticipating strategy under stationary competition. Our algorithm obtains $widetilde O(sqrt T)$-regret if the bids are all revealed at the end of each round.
  arXiv  Detail & Related papers  (2022-05-29T04:32:05Z)
• 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)
• Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns [115.96295568115251]
  We study the problem of automating the online joint bid/daily budget optimization of pay-per-click advertising campaigns over multiple channels. For every campaign, we capture the dependency of the number of clicks on the bid and daily budget by Gaussian Processes. We design four algorithms and show that they suffer from a regret that is upper bounded with high probability as O(sqrtT) We present the results of the adoption of our algorithms in a real-world application with a daily average spent of 1,000 Euros for more than one year.
  arXiv  Detail & Related papers  (2020-03-03T11:07:38Z)
• Online Causal Inference for Advertising in Real-Time Bidding Auctions [1.9336815376402723]
  This paper proposes a new approach to perform causal inference on advertising bought through real-time bidding systems. We first show that the effects of advertising are identified by the optimal bids. We introduce an adapted Thompson sampling (TS) algorithm to solve a multi-armed bandit problem.
  arXiv  Detail & Related papers  (2019-08-22T21:13:03Z)

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.