Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search

• URL: http://arxiv.org/abs/2106.04075v1
• Date: Tue, 8 Jun 2021 03:18:28 GMT
• Title: Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search
• Authors: Ziyu Guan, Hongchang Wu, Qingyu Cao, Hao Liu, Wei Zhao, Sheng Li, Cai Xu, Guang Qiu, Jian Xu, Bo Zheng
• Abstract summary: We propose a novel multi-objective cooperative bid optimization formulation called Multi-Agent Cooperative bidding Games (MACG) A global objective to maximize the overall profit of all advertisements is added in order to encourage better cooperation and also to protect self-bidding advertisers. offline experiments and online A/B tests conducted on the Taobao platform indicate both single advertiser's objective and global profit have been significantly improved.
• Score: 26.117969395228503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bid optimization for online advertising from single advertiser's perspective has been thoroughly investigated in both academic research and industrial practice. However, existing work typically assume competitors do not change their bids, i.e., the wining price is fixed, leading to poor performance of the derived solution. Although a few studies use multi-agent reinforcement learning to set up a cooperative game, they still suffer the following drawbacks: (1) They fail to avoid collusion solutions where all the advertisers involved in an auction collude to bid an extremely low price on purpose. (2) Previous works cannot well handle the underlying complex bidding environment, leading to poor model convergence. This problem could be amplified when handling multiple objectives of advertisers which are practical demands but not considered by previous work. In this paper, we propose a novel multi-objective cooperative bid optimization formulation called Multi-Agent Cooperative bidding Games (MACG). MACG sets up a carefully designed multi-objective optimization framework where different objectives of advertisers are incorporated. A global objective to maximize the overall profit of all advertisements is added in order to encourage better cooperation and also to protect self-bidding advertisers. To avoid collusion, we also introduce an extra platform revenue constraint. We analyze the optimal functional form of the bidding formula theoretically and design a policy network accordingly to generate auction-level bids. Then we design an efficient multi-agent evolutionary strategy for model optimization. Offline experiments and online A/B tests conducted on the Taobao platform indicate both single advertiser's objective and global profit have been significantly improved compared to state-of-art methods.

Related papers

• 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)
• Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance [96.73189436721465]
  We first present a multi-agent RL (MARL) method for multi-order execution considering practical constraints. We propose a learnable multi-round communication protocol, for the agents communicating the intended actions with each other. Experiments on the data from two real-world markets have illustrated superior performance with significantly better collaboration effectiveness.
  arXiv  Detail & Related papers  (2023-07-06T16:45:40Z)
• Demystifying Advertising Campaign Bid Recommendation: A Constraint target CPA Goal Optimization [19.857681941728597]
  This paper presents a bid optimization scenario to achieve the desired cost-per-acquisition (tCPA) goals for advertisers. We build the optimization engine to make a decision by solving the rigorously formalized constrained optimization problem. The proposed model can naturally recommend the bid that meets the advertisers' expectations by making inference over advertisers' historical auction behaviors.
  arXiv  Detail & Related papers  (2022-12-26T07:43:26Z)
• Functional Optimization Reinforcement Learning for Real-Time Bidding [14.5826735379053]
  Real-time bidding is the new paradigm of programmatic advertising. Existing approaches are struggling to provide a satisfactory solution for bidding optimization. This paper proposes a multi-agent reinforcement learning architecture for RTB with functional optimization.
  arXiv  Detail & Related papers  (2022-06-25T06:12:17Z)
• 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)
• MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding [47.555870679348416]
  We propose a Multi-ecTive Actor-Critics algorithm named MoTiAC for the problem of bidding optimization with various goals. Unlike previous RL models, the proposed MoTiAC can simultaneously fulfill multi-objective tasks in complicated bidding environments.
  arXiv  Detail & Related papers  (2020-02-18T07:16:39Z)

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.