no code implementations • 26 Feb 2024 • Naicheng Guo, Hongwei Cheng, Qianqiao Liang, Linxun Chen, Bing Han
SBR has been primarily dominated by Graph Neural Networks, which have achieved many successful outcomes due to their ability to capture both the implicit and explicit relationships between adjacent behaviors.
Natural Language Understanding Session-Based Recommendations +2
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 • 24 Oct 2022 • Xiaolin Zheng, Rui Wu, Zhongxuan Han, Chaochao Chen, Linxun Chen, Bing Han
HICG utilizes multiple types of user behaviors in the sessions to construct heterogeneous graphs, and captures users' current interests with their long-term preferences by effectively crossing the heterogeneous information on the graphs.