Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach

no code implementations • 8 Nov 2023 • Wujiang Xu, Xuying Ning, Wenfang Lin, Mingming Ha, Qiongxu Ma, Qianqiao Liang, Xuewen Tao, Linxun Chen, Bing Han, Minnan Luo

Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems.

Denoising Sequential Recommendation

Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations

no code implementations • 6 Jan 2023 • Xuewen Tao, Mingming Ha, Xiaobo Guo, Qiongxu Ma, Hongwei Cheng, Wenfang Lin

The general idea of multi-task learning is designing kinds of global parameter sharing mechanism and task-specific feature extractor to improve the performance of all tasks.

Multi-Task Learning Representation Learning

An Adaptive Oversampling Learning Method for Class-Imbalanced Fault Diagnostics and Prognostics

no code implementations • 19 Nov 2018 • Wenfang Lin, Zhen-Yu Wu, Yang Ji

Data-driven fault diagnostics and prognostics suffers from class-imbalance problem in industrial systems and it raises challenges to common machine learning algorithms as it becomes difficult to learn the features of the minority class samples.

Imputation