Joint Auction in the Online Advertising Market

TL;DR

This paper introduces a novel joint auction model for online advertising that enables store owners and brand suppliers to collaboratively bid for ad slots, using a neural network architecture to optimize revenue and ensure incentive compatibility.

Contribution

The paper proposes JRegNet, a neural network-based mechanism for joint advertising auctions, addressing the limitations of traditional auction methods in this new scenario.

Findings

Significant revenue improvement over baseline auctions

Effective neural network mechanism for joint bidding

Near incentive compatibility achieved

Abstract

Online advertising is a primary source of income for e-commerce platforms. In the current advertising pattern, the oriented targets are the online store owners who are willing to pay extra fees to enhance the position of their stores. On the other hand, brand suppliers are also desirable to advertise their products in stores to boost brand sales. However, the currently used advertising mode cannot satisfy the demand of both stores and brand suppliers simultaneously. To address this, we innovatively propose a joint advertising model termed Joint Auction, allowing brand suppliers and stores to collaboratively bid for advertising slots, catering to both their needs. However, conventional advertising auction mechanisms are not suitable for this novel scenario. In this paper, we propose JRegNet, a neural network architecture for the optimal joint auction design, to generate mechanisms that can achieve the optimal revenue and guarantee near dominant strategy incentive compatibility and individual rationality. Finally, multiple experiments are conducted on synthetic and real data to demonstrate that our proposed joint auction significantly improves platform revenue compared to the known baselines.