no code implementations • 21 Oct 2023 • Yongjing Hao, Pengpeng Zhao, Junhua Fang, Jianfeng Qu, Guanfeng Liu, Fuzhen Zhuang, Victor S. Sheng, Xiaofang Zhou
In this paper, we propose a Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for sequential recommendation, which applies the meta-optimized two-step training strategy to adaptive generate contrastive views.
no code implementations • 3 Jun 2023 • Tongyue Shi, Zhilong Zhang, Wentie Liu, Junhua Fang, Jianguo Hao, Shuai Jin, Huiying Zhao, Guilan Kong
This study employed the MIMIC-IV database as data source to investigate the use of dynamic, high-frequency, multivariate time-series vital signs data, including temperature, heart rate, mean blood pressure, respiratory rate, and SpO2, monitored first 8 hours data in the ICU stay.
1 code implementation • 7 May 2023 • Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Junhua Fang, Guanfeng Liu, Yanchi Liu, Victor S. Sheng, Xiaofang Zhou
Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data.
1 code implementation • 16 Apr 2023 • Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao, Junhua Fang, Fuzhen Zhuang, Guanfeng Liu, Victor Sheng
By applying both data augmentation and learnable model augmentation operations, this work innovates the standard CL framework by contrasting data and model augmented views for adaptively capturing the informative features hidden in stochastic data augmentation.