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Real-time bidding with multi-agent reinforcement learning in display advertising

Jin, J; Song, C; Li, H; Gai, K; Wang, J; Zhang, W; (2018) Real-time bidding with multi-agent reinforcement learning in display advertising. In: (Proceedings) CIKM 2018. (pp. pp. 2193-2202). ArXiv Green open access

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Abstract

Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a strategic response with respect to other advertisers bidding in the market. In this paper, we formulate bidding optimization with multi-agent reinforcement learning. To deal with a large number of advertisers, we propose a clustering method and assign each cluster with a strategic bidding agent. A practical Distributed Coordinated Multi-Agent Bidding (DCMAB) has been proposed and implemented to balance the tradeoff between the competition and cooperation among advertisers. The empirical study on our industry-scaled real-world data has demonstrated the effectiveness of our methods. Our results show cluster-based bidding would largely outperform single-agent and bandit approaches, and the coordinated bidding achieves better overall objectives than purely self-interested bidding agents.

Type: Proceedings paper
Title: Real-time bidding with multi-agent reinforcement learning in display advertising
Event: CIKM 2018
ISBN-13: 9781450360142
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3269206.3272021
Publisher version: https://doi.org/10.1145/3269206.3272021
Language: English
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10066097
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