59 citations · 110 across the 4 of their papers we have counts for
4 papers
No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods
Zhengxing Cheng, Yuheng Huang, Zhixuan Zhang +2
Given the ubiquity of multi-task in practical systems, Multi-Task Learning (MTL) has found widespread application across diverse domains. In real-world scenarios, these tasks often…
Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
Zhifang Fan, Dan Ou, Yulong Gu +7
Modeling user's historical feedback is essential for Click-Through Rate Prediction in personalized search and recommendation. Existing methods usually only model users' positive fe…
Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
Xiang Li, Chao Wang, Jiwei Tan +3
For better user experience and business effectiveness, Click-Through Rate (CTR) prediction has been one of the most important tasks in E-commerce. Although extensive CTR prediction…
Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks
Yabo Ni, Dan Ou, Shichen Liu +4
Tasks such as search and recommendation have become increas- ingly important for E-commerce to deal with the information over- load problem. To meet the diverse needs of di erent u…