A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction

TL;DR

This paper introduces DSPN, a deep learning model that predicts advertiser satisfaction and intent simultaneously, improving understanding and optimization of advertising strategies on e-commerce platforms.

Contribution

The paper presents a novel two-stage deep network that models advertiser intent and satisfaction jointly, enhancing prediction accuracy and interpretability over existing methods.

Findings

DSPN outperforms state-of-the-art baselines in AUC metrics.

DSPN accurately predicts advertiser satisfaction.

The model provides explainable insights into advertiser intent.

Abstract

For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue. Therefore, providing better services for advertisers is essential for the long-term prosperity for e-commerce platforms. To achieve this goal, the ad platform needs to have an in-depth understanding of advertisers in terms of both their marketing intents and satisfaction over the advertising performance, based on which further optimization could be carried out to service the advertisers in the correct direction. In this paper, we propose a novel Deep Satisfaction Prediction Network (DSPN), which models advertiser intent and satisfaction simultaneously. It employs a two-stage network structure where advertiser intent vector and satisfaction are jointly learned by considering the features of advertiser's action information and advertising performance indicators. Experiments on an Alibaba advertisement dataset and online evaluations show that our proposed DSPN outperforms state-of-the-art baselines and has stable performance in terms of AUC in the online environment. Further analyses show that DSPN not only predicts advertisers' satisfaction accurately but also learns an explainable advertiser intent, revealing the opportunities to optimize the advertising performance further.