  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.
