Multi-task learning (MTL) has been successfully implemented in many real-world applications, which aims to simultaneously solve multiple tasks with a single model. The general idea of multi-task learning is designing kinds of global parameter sharing mechanism and task-specific feature extractor to improve the performance of all tasks. However, sequential dependence between tasks are rarely studied but frequently encountered in e-commence online recommendation, e.g. impression, click and conversion on displayed product. There is few theoretical work on this problem and biased optimization object adopted in most MTL methods deteriorates online performance. Besides, challenge still remains in balancing the trade-off between various tasks and effectively learn common and specific representation. In this paper, we first analyze sequential dependence MTL from rigorous mathematical perspective and design a dependence task learning loss to provide an unbiased optimizing object. And we propose a Task Aware Feature Extraction (TAFE) framework for sequential dependence MTL, which enables to selectively reconstruct implicit shared representations from a sample-wise view and extract explicit task-specific information in an more efficient way. Extensive experiments on offline datasets and online A/B implementation demonstrate the effectiveness of our proposed TAFE.

本文提出了自适应模式提取多任务框架(APEM)，该框架采用DeepAuto Group Transformer模块和明确的模式选择器，以提高多任务学习的性能和灵活性，通过公共和实际金融服务数据集的实证评估表明，APEM优于现有的多任务学习方法， 更重要的是，我们探讨了APEM的在线性能在实际工业级推荐方案中的应用。

多阶段转化估计的自适应模式提取多任务学习