1 code implementation • 5 Aug 2022 • Yongxiang Tang, Wentao Bai, Guilin Li, Xialong Liu, Yu Zhang
In this paper, we proposed the Customizable Recall@N Optimization Loss (CROLoss), a loss function that can directly optimize the Recall@N metrics and is customizable for different choices of N. This proposed CROLoss formulation defines a more generalized loss function space, covering most of the conventional loss functions as special cases.
no code implementations • 4 Jun 2022 • Xiaochen Li, Xin Song, Pengjia Yuan, Xialong Liu, Yu Zhang
In this paper, we focus on a new type of user interest, i. e., user retargeting interest.
no code implementations • 25 Apr 2022 • Xiaochen Li, Rui Zhong, Jian Liang, Xialong Liu, Yu Zhang
Rich user behavior information is of great importance for capturing and understanding user interest in click-through rate (CTR) prediction.