Coupon has been a major marketing tool that promotes sales for new and repeated buyers and proven effective in numerous realistic scenarios. In this paper, we mainly focus on the problem of designing coupons to maximize the revenue in second-price auction. 

Firstly, we derive the dominant strategies of bidders if they are provided with coupons in second-price auction and prove that the revenue optimization problem with coupons for all the bidders is NP-complete. Secondly, we cast the problem of designing coupons to maximize revenue into a learning framework. With well-designed loss functions, we perform theoretical analysis of its properties and propose corresponding algorithms to solve the problem. Finally, with both synthetic data and industrial data, extensive experiments are conducted to demonstrate their effectiveness.


Learning to Design Coupons in Online Advertising Markets

Advertising has become a dominant source of revenue generation on the Internet. Billions of advertisement slots are sold via auctions. And there are many pricing methods ,e.g., CPM (cost-per-mille), CPC (cost-per-click), CPA (cost-per-action), OCPM (optimized cost-per-mille) and so on. In this paper, we study the OCPM method (i.e., advertisers bid for conversions while pay per mille) under VCG auction. However, automatically bid in each view to maximize advertisers' conversions while still meet their target cost-per-conversion in feeds is difficult. To deal with these difficulties, we propose a reinforcement learning framework, i.e., RSDRL (ROI-sensitive distributional reinforcement learning). By making full use of the characteristics of auction rules which are missed by other methods, we design a reward function to surrogate conversion events and a bid generation method based on theoretical results. We also provide some theoretical results to guide hyperparameter tuning. Last, we validate RSDRL on a large industrial dataset with millions of auctions. Plenty of experiments (both online and offline) are used to evaluate the performance of our framework and RSDRL yields substantially better results than compared algorithms.


Optimized Cost per Mille in Feeds Advertising

technical paper

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

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AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

Xun Wang

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Learning to Design Coupons in Online Advertising Markets

Optimized Cost per Mille in Feeds Advertising

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