3 code implementations • ACL 2022 • Yanzeng Li, Jiangxia Cao, Xin Cong, Zhenyu Zhang, Bowen Yu, Hongsong Zhu, Tingwen Liu
Chinese pre-trained language models usually exploit contextual character information to learn representations, while ignoring the linguistics knowledge, e. g., word and sentence information.
no code implementations • 30 Mar 2024 • Wentao Xu, Qianqian Xie, Shuo Yang, Jiangxia Cao, Shuchao Pang
However, they still neglect the following two points: (1) The content semantic is a universal world knowledge; how do we extract the multi-aspect semantic information to empower different domains?
no code implementations • 23 Jan 2024 • XiaoDong Li, Jiawei Sheng, Jiangxia Cao, Wenyuan Zhang, Quangang Li, Tingwen Liu
Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by transferring the user preference derived from the source domain.
no code implementations • 20 Apr 2023 • Gehang Zhang, Bowen Yu, Jiangxia Cao, Xinghua Zhang, Jiawei Sheng, Chuan Zhou, Tingwen Liu
Graph contrastive learning (GCL) has recently achieved substantial advancements.
1 code implementation • 8 Apr 2023 • Jiangxia Cao, Xin Cong, Jiawei Sheng, Tingwen Liu, Bin Wang
Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains.
no code implementations • 5 Apr 2023 • Shiyao Cui, Jiangxia Cao, Xin Cong, Jiawei Sheng, Quangang Li, Tingwen Liu, Jinqiao Shi
For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction.
1 code implementation • 31 Mar 2022 • Jiangxia Cao, Jiawei Sheng, Xin Cong, Tingwen Liu, Bin Wang
As a promising way, Cross-Domain Recommendation (CDR) has attracted a surge of interest, which aims to transfer the user preferences observed in the source domain to make recommendations in the target domain.
1 code implementation • 8 Jul 2021 • Jiangxia Cao, Xixun Lin, Xin Cong, Shu Guo, Hengzhu Tang, Tingwen Liu, Bin Wang
A temporal interaction network consists of a series of chronological interactions between users and items.
1 code implementation • 10 Dec 2020 • Jiangxia Cao, Xixun Lin, Shu Guo, Luchen Liu, Tingwen Liu, Bin Wang
However, the global properties of bipartite graph, including community structures of homogeneous nodes and long-range dependencies of heterogeneous nodes, are not well preserved.
no code implementations • 28 Mar 2020 • Hengzhu Tang, Yanan Cao, Zhen-Yu Zhang, Jiangxia Cao, Fang Fang, Shi Wang, Pengfei Yin
In this paper, we propose a Hierarchical Inference Network (HIN) to make full use of the abundant information from entity level, sentence level and document level.
Ranked #51 on Relation Extraction on DocRED