  Advertisers play an essential role in many e-commerce platforms like Taobao
and Amazon. Fulfilling their marketing needs and supporting their business
growth is critical to the long-term prosperity of platform economies. However,
compared with extensive studies on user modeling such as click-through rate
predictions, much less attention has been drawn to advertisers, especially in
terms of understanding their diverse demands and performance. Different from
user modeling, advertiser modeling generally involves many kinds of tasks (e.g.
predictions of advertisers' expenditure, active-rate, or total impressions of
promoted products). In addition, major e-commerce platforms often provide
multiple marketing scenarios (e.g. Sponsored Search, Display Ads, Live
Streaming Ads) while advertisers' behavior tend to be dispersed among many of
them. This raises the necessity of multi-task and multi-scenario consideration
in comprehensive advertiser modeling, which faces the following challenges:
First, one model per scenario or per task simply doesn't scale; Second, it is
particularly hard to model new or minor scenarios with limited data samples;
Third, inter-scenario correlations are complicated, and may vary given
different tasks. To tackle these challenges, we propose a multi-scenario
multi-task meta learning approach (M2M) which simultaneously predicts multiple
tasks in multiple advertising scenarios.
