Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling

TL;DR

This paper introduces M2M, a meta learning approach designed to improve advertiser modeling across multiple scenarios and tasks, addressing scalability, data scarcity, and inter-scenario correlation challenges in e-commerce advertising.

Contribution

The paper presents a novel multi-scenario multi-task meta learning framework that enhances advertiser modeling by effectively handling diverse tasks and scenarios with limited data.

Findings

M2M outperforms baseline models in multi-scenario advertiser prediction tasks.

The approach effectively models new or minor scenarios with limited data.

Inter-scenario correlations are better captured using the proposed method.

Abstract

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.