Search Results for author: Jiangxia Cao

Found 10 papers, 5 papers with code

Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph

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.

Language Modelling Sentence

Enhancing Content-based Recommendation via Large Language Model

no code implementations30 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?

Language Modelling Large Language Model +1

CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process

no code implementations23 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.


Contrastive Cross-Domain Sequential Recommendation

1 code implementation8 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.

Graph Neural Network Sequential Recommendation

Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck

no code implementations5 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.

named-entity-recognition Named Entity Recognition +3

Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck

1 code implementation31 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.

Recommendation Systems

Deep Structural Point Process for Learning Temporal Interaction Networks

1 code implementation8 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.

Bipartite Graph Embedding via Mutual Information Maximization

1 code implementation10 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.

Graph Embedding Link Prediction

HIN: Hierarchical Inference Network for Document-Level Relation Extraction

no code implementations28 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.

Document-level Relation Extraction Relation +2

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