Multi-objective optimization for sponsored search

Wang, Yilei; Wei, Bingzheng; Yan, Jun; Chen, Zheng; Du, Qiao

doi:10.1145/2351356.2351359

Cited by 3 publications

(1 citation statement)

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“…In [20], a Multi-Objective Optimization (MOO) framework is proposed to optimize the profit of platform, advertisers and query users together by tuning the ranking function (i.e., auction mechanism). Our work is orthogonal to theirs in that we aim to achieve different objectives of advertisers and keep auctions healthy by optimizing their bidding policies while keeping the auction mechanism fixed.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

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

Guan

Cao

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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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. Evolutionary strategy does not need to model the underlying environment explicitly and is more suitable for bid 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.

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Section: Multi-objective Optimizationmentioning

confidence: 99%