.png){kind=icon}
返回
Multi-Task Learning with Sequential Dependence Toward Industrial Applications: A Systematic Formulation
.png){kind=icon}
PRE
AI
DOI：10.1145/3640468.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Multi-task learning
sequential dependency
negative transfer
industrial applications
期刊
IF:
4.8
论文数:
1.3K
被引数:
4.4K
机构
引用论文
A Cross-Modal Investigation of Spatial Performances in Patients with Unilateral Cerebral Disease.png)
Cortex
OAAI
CortexIF0
OAAI分享收藏TAP: Traffic Accident Profiling via Multi-Task Spatio-Temporal Graph Representation LearningTAP: 通过多任务时空图表示学习进行交通事故分析
PREAI
分享收藏Assembly of ring canals in the male germ line from structural components of the contractile ring
PREAI
分享收藏
分享收藏
查看更多内容