Hybrid Advertising in the Sponsored Search

TL;DR

This paper introduces Hybrid Advertising, a new auction model for online ads that combines store and bundle ads, improving revenue and user engagement through a neural network-based mechanism.

Contribution

The paper proposes Hybrid Advertising, a novel auction framework and the HRegNet neural network to optimize ad allocations, enhancing revenue and user experience.

Findings

HRegNet outperforms baseline methods in revenue generation

Hybrid Advertising increases click-through rates by combining ad models

Experiments on synthetic and real data validate effectiveness

Abstract

Online advertisements are a primary revenue source for e-commerce platforms. Traditional advertising models are store-centric, selecting winning stores through auction mechanisms. Recently, a new approach known as joint advertising has emerged, which presents sponsored bundles combining one store and one brand in ad slots. Unlike traditional models, joint advertising allows platforms to collect payments from both brands and stores. However, each of these two advertising models appeals to distinct user groups, leading to low click-through rates when users encounter an undesirable advertising model. To address this limitation and enhance generality, we propose a novel advertising model called ''Hybrid Advertising''. In this model, each ad slot can be allocated to either an independent store or a bundle. To find the optimal auction mechanisms in hybrid advertising, while ensuring nearly dominant strategy incentive compatibility and individual rationality, we introduce the Hybrid Regret Network (HRegNet), a neural network architecture designed for this purpose. Extensive experiments on both synthetic and real-world data demonstrate that the mechanisms generated by HRegNet significantly improve platform revenue compared to established baseline methods.