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Title: Optimal real-time bidding for display advertising
Author: Zhang, W.
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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Real-Time Bidding (RTB) is revolutionising display advertising by facilitating a real-time auction for each ad impression. As they are able to use impression-level data, such as user cookies and context information, advertisers can adaptively bid for each ad impression. Therefore, it is important that an advertiser designs an effective bidding strategy which can be abstracted as a function - mapping from the information of a specific ad impression to the bid price. Exactly how this bidding function should be designed is a non-trivial problem. It is a problem which involves multiple factors, such as the campaign-specific key performance indicator (KPI), the campaign lifetime auction volume and the budget. This thesis is focused on the design of automatic solutions to this problem of creating optimised bidding strategies for RTB auctions: strategies which are optimal, that is, from the perspective of an advertiser agent - to maximise the campaign's KPI in relation to the constraints of the auction volume and the budget. The problem is mathematically formulated as a functional optimisation framework where the optimal bidding function can be derived without any functional form restriction. Beyond single-campaign bid optimisation, the proposed framework can be extended to multi-campaign cases, where a portfolio-optimisation solution of auction volume reallocation is performed to maximise the overall profit with a controlled risk. On the model learning side, an unbiased learning scheme is proposed to address the data bias problem resulting from the ad auction selection, where we derive a "bid-aware'' gradient descent algorithm to train unbiased models. Moreover, the robustness of achieving the expected KPIs in a dynamic RTB market is solved with a feedback control mechanism for bid adjustment. To support the theoretic derivations, extensive experiments are carried out based on large-scale real-world data. The proposed solutions have been deployed in three commercial RTB systems in China and the United States. The online A/B tests have demonstrated substantial improvement of the proposed solutions over strong baselines.
Supervisor: Wang, J. ; Robertson, S. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available