1.2k citations · 2.8k across the 60 of their papers we have counts for
17 papers · 1 filter
Graph Learning and Its Advancements on Large Language Models: A Holistic Survey
Shaopeng Wei, Jun Wang, Yu Zhao +6
Graph learning is a prevalent domain that endeavors to learn the intricate relationships among nodes and the topological structure of graphs. Over the years, graph learning has tra…
Pruning Pre-trained Language Models Without Fine-Tuning
Ting Jiang, Deqing Wang, Fuzhen Zhuang +2
To overcome the overparameterized problem in Pre-trained Language Models (PLMs), pruning is widely used as a simple and straightforward compression method by directly removing unim…
A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis
Wei Chen, Jinglong Du, Zhao Zhang +2
Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect bo…
Customized Conversational Recommender Systems
Shuokai Li, Yongchun Zhu, Ruobing Xie +5
Conversational recommender systems (CRS) aim to capture user's current intentions and provide recommendations through real-time multi-turn conversational interactions. As a human-m…
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
Zhenwei Tang, Shichao Pei, Zhao Zhang +4
Most real-world knowledge graphs (KG) are far from complete and comprehensive. This problem has motivated efforts in predicting the most plausible missing facts to complete a given…
Detect Professional Malicious User with Metric Learning in Recommender Systems
Yuanbo Xu, Yongjian Yang, En Wang +2
In e-commerce, online retailers are usually suffering from professional malicious users (PMUs), who utilize negative reviews and low ratings to their consumed products on purpose t…
TAR: Neural Logical Reasoning across TBox and ABox
Zhenwei Tang, Shichao Pei, Xi Peng +3
Many ontologies, i.e., Description Logic (DL) knowledge bases, have been developed to provide rich knowledge about various domains. An ontology consists of an ABox, i.e., assertion…
Personalized Prompt for Sequential Recommendation
Yiqing Wu, Ruobing Xie, Yongchun Zhu +4
Pre-training models have shown their power in sequential recommendation. Recently, prompt has been widely explored and verified for tuning in NLP pre-training, which could help to…
Selective Fairness in Recommendation via Prompts
Yiqing Wu, Ruobing Xie, Yongchun Zhu +5
Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and user…
Exploiting Global and Local Hierarchies for Hierarchical Text Classification
Ting Jiang, Deqing Wang, Leilei Sun +3
Hierarchical text classification aims to leverage label hierarchy in multi-label text classification. Existing methods encode label hierarchy in a global view, where label hierarch…
Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks
Yu Zhao, Shaopeng Wei, Yu Guo +6
Predicting the bankruptcy risk of small and medium-sized enterprises (SMEs) is an important step for financial institutions when making decisions about loans. Existing studies in b…
Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks
Yu Zhao, Shaopeng Wei, Huaming Du +5
Bi-type multi-relational heterogeneous graph (BMHG) is one of the most common graphs in practice, for example, academic networks, e-commerce user behavior graph and enterprise know…
Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks
Yu Zhao, Huaming Du, Ying Liu +6
Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent f…
Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection
Yongchun Zhu, Dongbo Xi, Bowen Song +4
With the explosive growth of the e-commerce industry, detecting online transaction fraud in real-world applications has become increasingly important to the development of e-commer…
Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple Sources
Yongchun Zhu, Fuzhen Zhuang, Deqing Wang
While Unsupervised Domain Adaptation (UDA) algorithms, i.e., there are only labeled data from source domains, have been actively studied in recent years, most algorithms and theore…
Multi-Representation Adaptation Network for Cross-domain Image Classification
Yongchun Zhu, Fuzhen Zhuang, Jindong Wang +4
In image classification, it is often expensive and time-consuming to acquire sufficient labels. To solve this problem, domain adaptation often provides an attractive option given a…
PromptBERT: Improving BERT Sentence Embeddings with Prompts
Ting Jiang, Jian Jiao, Shaohan Huang +7
We propose PromptBERT, a novel contrastive learning method for learning better sentence representation. We firstly analyze the drawback of current sentence embedding from original…