no code implementations • 24 May 2024 • Mingming Ha, Xuewen Tao, Wenfang Lin, Qionxu Ma, Wujiang Xu, Linxun Chen
In most practical applications such as recommendation systems, display advertising, and so forth, the collected data often contains missing values and those missing values are generally missing-not-at-random, which deteriorates the prediction performance of models.
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.
1 code implementation • 8 Nov 2023 • Wujiang Xu, Qitian Wu, Runzhong Wang, Mingming Ha, Qiongxu Ma, Linxun Chen, Bing Han, Junchi Yan
To address these challenges under open-world assumptions, we design an \textbf{A}daptive \textbf{M}ulti-\textbf{I}nterest \textbf{D}ebiasing framework for cross-domain sequential recommendation (\textbf{AMID}), which consists of a multi-interest information module (\textbf{MIM}) and a doubly robust estimator (\textbf{DRE}).
no code implementations • 12 Feb 2023 • Wujiang Xu, Shaoshuai Li, Mingming Ha, Xiaobo Guo, Qiongxu Ma, Xiaolei Liu, Linxun Chen, Zhenfeng Zhu
To tackle the aforementioned issues, we propose a simple-yet-effective neural node matching based framework for more general CDR settings, i. e., only (few) partially overlapped users exist across domains and most overlapped as well as non-overlapped users do have sparse interactions.
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.
no code implementations • 30 Nov 2022 • Ying Chen, Siwei Qiang, Mingming Ha, Xiaolei Liu, Shaoshuai Li, Lingfeng Yuan, Xiaobo Guo, Zhenfeng Zhu
Differing from homogeneous graph, DA in heterogeneous graph has greater challenges: heterogeneity of information requires DA strategies to effectively handle heterogeneous relations, which considers the information contribution of different types of neighbors and edges to the target nodes.