Multi-task learning (MTL) is widely used in the online recommendation and financial services for multi-step conversion estimation, but current works often overlook the sequential dependence among tasks. In particular, sequential dependence multi-task learning (SDMTL) faces challenges in dealing with complex task correlations and extracting valuable information in real-world scenarios, leading to negative transfer and a deterioration in the performance. Herein, a systematic learning paradigm of the SDMTL problem is established for the first time, which applies to more general multi-step conversion scenarios with longer conversion paths or various task dependence relationships. Meanwhile, an SDMTL architecture, named Task-Aware Feature Extraction (TAFE), is designed to enable the dynamic task representation learning from a sample-wise view. TAFE selectively reconstructs the implicit shared information corresponding to each sample case and performs the explicit task-specific extraction under dependence constraints, which can avoid the negative transfer, resulting in more effective information sharing and joint representation learning. Extensive experiment results demonstrate the effectiveness and applicability of the proposed theoretical and implementation frameworks. Furthermore, the online evaluations at MYbank showed that TAFE had an average increase of 9.22% and 3.76% in various scenarios on the post-view click-through & conversion rate (CTCVR) estimation task. Currently, TAFE is deployed in an online platform to provide various traffic services.

多任务学习（MTL）广泛应用于在线推荐和金融服务中进行多步转化估计，但当前的工作经常忽视任务之间的顺序依赖性。特别是，顺序依赖多任务学习（SDMTL）在处理复杂的任务相关性和在现实场景中提取有价值的信息方面面临挑战，导致负迁移和性能恶化。这里，首次建立了SDMTL问题的系统学习范式，适用于更一般的具有较长转换路径或各种任务依赖关系的多步转换场景。同时，名为任务感知特征提取（TAFE）的 SDMTL 架构旨在实现从样本视图进行动态任务表示学习。TAFE选择性地重构每个样本对应的隐式共享信息，并在依赖约束下进行显式特定任务提取，可以避免负迁移，从而实现更有效的信息共享和联合表示学习。大量的实验结果证明了所提出的理论和实施框架的有效性和适用性。此外，网商银行在线评测显示，TAFE 在观看后点击率和转化率（CTCVR）估算任务上，在各种场景下平均提升了 9.22% 和 3.76%。目前，TAFE部署在线上平台，提供各类流量服务。