80 citations · 481 across the 41 of their papers we have counts for
41 papers
Predicting Financial Literacy via Semi-supervised Learning
David Hason Rudd, Huan Huo, Guandong Xu
Financial literacy (FL) represents a person's ability to turn assets into income, and understanding digital currencies has been added to the modern definition. FL can be predicted…
Leveraged Mel spectrograms using Harmonic and Percussive Components in Speech Emotion Recognition
David Hason Rudd, Huan Huo, Guandong Xu
Speech Emotion Recognition (SER) affective technology enables the intelligent embedded devices to interact with sensitivity. Similarly, call centre employees recognise customers' e…
An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance
David Hason Rudd, Huan Huo, Guandong Xu
Emotion recognition (ER) from speech signals is a robust approach since it cannot be imitated like facial expression or text based sentiment analysis. Valuable information underlyi…
Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation
Xin Xia, Junliang Yu, Guandong Xu +1
On-device recommender systems recently have garnered increasing attention due to their advantages of providing prompt response and securing privacy. To stay current with evolving u…
LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following
Kaize Shi, Xueyao Sun, Dingxian Wang +3
E-commerce authoring entails creating engaging, diverse, and targeted content to enhance preference elicitation and retrieval experience. While Large Language Models (LLMs) have re…
Counterfactual Explanation for Fairness in Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu +2
Fairness-aware recommendation eliminates discrimination issues to build trustworthy recommendation systems.Explaining the causes of unfair recommendations is critical, as it promot…
Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions
David Hason Rudd, Huan Huo, Guandong Xu
Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Customer acquisition cost can be five to six times that…
Causal Analysis of Customer Churn Using Deep Learning
David Hason Rudd, Huan Huo, Guandong Xu
Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Two main business marketing strategies play vital roles…
Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation
Jing Long, Tong Chen, Nguyen Quoc Viet Hung +3
As an indispensable personalized service in Location-based Social Networks (LBSNs), the next Point-of-Interest (POI) recommendation aims to help people discover attractive and inte…
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows
Tri Dung Duong, Qian Li, Guandong Xu
Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome. The generated samples can act as ins…
Achieving Counterfactual Fairness with Imperfect Structural Causal Model
Tri Dung Duong, Qian Li, Guandong Xu
Counterfactual fairness alleviates the discrimination between the model prediction toward an individual in the actual world (observational data) and that in counterfactual world (i…
Self-supervised Hypergraph Representation Learning for Sociological Analysis
Xiangguo Sun, Hong Cheng, Bo Liu +4
Modern sociology has profoundly uncovered many convincing social criteria for behavioural analysis. Unfortunately, many of them are too subjective to be measured and presented in o…
Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks
Dawei Xu, Haoran Yang, Marian-Andrei Rizoiu +1
The rapid advances in automation technologies, such as artificial intelligence (AI) and robotics, pose an increasing risk of automation for occupations, with a likely significant i…
Reinforced Path Reasoning for Counterfactual Explainable Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu +1
Counterfactual explanations interpret the recommendation mechanism via exploring how minimal alterations on items or users affect the recommendation decisions. Existing counterfact…
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning
Haoran Yang, Xiangyu Zhao, Muyang Li +2
Currently, graph learning models are indispensable tools to help researchers explore graph-structured data. In academia, using sufficient training data to optimize a graph model on…
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning
Haoran Yang, Hongxu Chen, Sixiao Zhang +4
Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation learning. The key to the success of graph contrastive learning is to acquire high-qu…
On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation
Xin Xia, Hongzhi Yin, Junliang Yu +3
Modern recommender systems operate in a fully server-based fashion. To cater to millions of users, the frequent model maintaining and the high-speed processing for concurrent user…
Dual Space Graph Contrastive Learning
Haoran Yang, Hongxu Chen, Shirui Pan +3
Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain. Graph contrastive l…
DialMed: A Dataset for Dialogue-based Medication Recommendation
Zhenfeng He, Yuqiang Han, Zhenqiu Ouyang +4
Medication recommendation is a crucial task for intelligent healthcare systems. Previous studies mainly recommend medications with electronic health records (EHRs). However, some d…
What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code
Yao Wan, Wei Zhao, Hongyu Zhang +3
Recently, many pre-trained language models for source code have been proposed to model the context of code and serve as a basis for downstream code intelligence tasks such as code…
Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu +3
Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users' t…
Graph Masked Autoencoders with Transformers
Sixiao Zhang, Hongxu Chen, Haoran Yang +3
Recently, transformers have shown promising performance in learning graph representations. However, there are still some challenges when applying transformers to real-world scenari…
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation
Sixiao Zhang, Hongxu Chen, Xiangguo Sun +2
Graph contrastive learning is the state-of-the-art unsupervised graph representation learning framework and has shown comparable performance with supervised approaches. However, ev…
Cross-Language Binary-Source Code Matching with Intermediate Representations
Yi Gui, Yao Wan, Hongyu Zhang +5
Binary-source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessm…