463 citations · 2.6k across the 123 of their papers we have counts for
123 papers
To Predict or to Reject: Causal Effect Estimation with Uncertainty on Networked Data
Hechuan Wen, Tong Chen, Li Kheng Chai +3
Due to the imbalanced nature of networked observational data, the causal effect predictions for some individuals can severely violate the positivity/overlap assumption, rendering u…
Learning Compact Compositional Embeddings via Regularized Pruning for Recommendation
Xurong Liang, Tong Chen, Quoc Viet Hung Nguyen +2
Latent factor models are the dominant backbones of contemporary recommender systems (RSs) given their performance advantages, where a unique vector embedding with a fixed dimension…
Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation
Guanhua Ye, Tong Chen, Quoc Viet Hung Nguyen +1
As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalized IoT service providers ha…
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…
Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph
Yi Liu, Hongrui Xuan, Bohan Li +3
Knowledge graphs (KGs) are commonly used as side information to enhance collaborative signals and improve recommendation quality. In the context of knowledge-aware recommendation (…
Graph Condensation for Inductive Node Representation Learning
Xinyi Gao, Tong Chen, Yilong Zang +4
Graph neural networks (GNNs) encounter significant computational challenges when handling large-scale graphs, which severely restricts their efficacy across diverse applications. T…
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection
Jie Liu, Mengting He, Xuequn Shang +3
Graph anomaly detection (GAD) has gained increasing attention in recent years due to its critical application in a wide range of domains, such as social networks, financial risk ma…
HeteFedRec: Federated Recommender Systems with Model Heterogeneity
Wei Yuan, Liang Qu, Lizhen Cui +3
Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems. However, most ex…
Variational Counterfactual Prediction under Runtime Domain Corruption
Hechuan Wen, Tong Chen, Li Kheng Chai +3
To date, various neural methods have been proposed for causal effect estimation based on observational data, where a default assumption is the same distribution and availability of…
Personalized Elastic Embedding Learning for On-Device Recommendation
Ruiqi Zheng, Liang Qu, Tong Chen +3
To address privacy concerns and reduce network latency, there has been a recent trend of compressing cumbersome recommendation models trained on the cloud and deploying compact rec…
Do as I can, not as I get
Shangfei Zheng, Hongzhi Yin, Tong Chen +3
This paper proposes a model called TMR to mine valuable information from simulated data environments. We intend to complete the submission of this paper.
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
Jintang Li, Sheng Tian, Ruofan Wu +6
The prevalence of large-scale graphs poses great challenges in time and storage for training and deploying graph neural networks (GNNs). Several recent works have explored solution…
Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures
Wei Yuan, Shilong Yuan, Chaoqun Yang +2
Federated recommender systems (FedRecs) have been widely explored recently due to their ability to protect user data privacy. In FedRecs, a central server collaboratively learns re…
KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment
Lingzhi Wang, Tong Chen, Wei Yuan +3
Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about…
Explicit Knowledge Graph Reasoning for Conversational Recommendation
Xuhui Ren, Tong Chen, Quoc Viet Hung Nguyen +3
Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests an…
Imbalanced Node Classification Beyond Homophilic Assumption
Jie Liu, Mengting He, Guangtao Wang +3
Imbalanced node classification widely exists in real-world networks where graph neural networks (GNNs) are usually highly inclined to majority classes and suffer from severe perfor…
Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling
Xuhui Ren, Wei Yuan, Tong Chen +3
Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences. Despite recent advances in KG-ba…
Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures
Wei Yuan, Quoc Viet Hung Nguyen, Tieke He +2
Federated Recommender Systems (FedRecs) are considered privacy-preserving techniques to collaboratively learn a recommendation model without sharing user data. Since all participan…
Automated Prompting for Non-overlapping Cross-domain Sequential Recommendation
Lei Guo, Chunxiao Wang, Xinhua Wang +2
Cross-domain Recommendation (CR) has been extensively studied in recent years to alleviate the data sparsity issue in recommender systems by utilizing different domain information.…
DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning
Shangfei Zheng, Hongzhi Yin, Tong Chen +3
Temporal knowledge graphs (TKGs) model the temporal evolution of events and have recently attracted increasing attention. Since TKGs are intrinsically incomplete, it is necessary t…
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…
Continuous Input Embedding Size Search For Recommender Systems
Yunke Qu, Tong Chen, Xiangyu Zhao +3
Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance. Latent factor models represent users and items as real-val…
TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT
Yuting Sun, Tong Chen, Quoc Viet Hung Nguyen +1
Monitoring and detecting abnormal events in cyber-physical systems is crucial to industrial production. With the prevalent deployment of the Industrial Internet of Things (IIoT), a…
Knowledge Enhancement for Contrastive Multi-Behavior Recommendation
Hongrui Xuan, Yi Liu, Bohan Li +1
A well-designed recommender system can accurately capture the attributes of users and items, reflecting the unique preferences of individuals. Traditional recommendation techniques…