449 citations · 768 across the 44 of their papers we have counts for
44 papers
Scaling Text2SQL via LLM-efficient Schema Filtering with Functional Dependency Graph Rerankers
Thanh Dat Hoang, Thanh Tam Nguyen, Thanh Trung Huynh +2
Most modern Text2SQL systems prompt large language models (LLMs) with entire schemas -- mostly column information -- alongside the user's question. While effective on small databas…
Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era
Thanh Tam Nguyen, Zhao Ren, Trinh Pham +4
The rapid advancement of large language models (LLMs) and multimodal learning has transformed digital content creation and manipulation. Traditional visual editing tools require si…
Scalable and Effective Negative Sample Generation for Hyperedge Prediction
Shilin Qu, Weiqing Wang, Yuan-Fang Li +2
Hyperedge prediction is crucial in hypergraph analysis for understanding complex multi-entity interactions in various web-based applications, including social networks and e-commer…
ID-Free Not Risk-Free: LLM-Powered Agents Unveil Risks in ID-Free Recommender Systems
Zongwei Wang, Min Gao, Junliang Yu +4
Recent advances in ID-free recommender systems have attracted significant attention for effectively addressing the cold start problem. However, their vulnerability to malicious att…
Heterogeneous Hypergraph Embedding for Recommendation Systems
Darnbi Sakong, Viet Hung Vu, Thanh Trung Huynh +4
Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is…
Scalable Dynamic Embedding Size Search for Streaming Recommendation
Yunke Qu, Liang Qu, Tong Chen +3
Recommender systems typically represent users and items by learning their embeddings, which are usually set to uniform dimensions and dominate the model parameters. However, real-w…
A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures
Thanh Tam Nguyen, Thanh Trung Huynh, Zhao Ren +4
As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and exp…
Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation
Jing Long, Tong Chen, Guanhua Ye +3
As an indispensable personalized service within Location-Based Social Networks (LBSNs), the Point-of-Interest (POI) recommendation aims to assist individuals in discovering attract…
Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation
Lei Guo, Ziang Lu, Junliang Yu +2
Cross-domain Recommendation (CDR) as one of the effective techniques in alleviating the data sparsity issues has been widely studied in recent years. However, previous works may ca…
Budgeted Embedding Table For Recommender Systems
Yunke Qu, Tong Chen, Quoc Viet Hung Nguyen +1
At the heart of contemporary recommender systems (RSs) are latent factor models that provide quality recommendation experience to users. These models use embedding vectors, which a…
Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation
Xinyi Gao, Wentao Zhang, Junliang Yu +4
Graph neural networks (GNNs) have exhibited exceptional efficacy in a diverse array of applications. However, the sheer size of large-scale graphs presents a significant challenge…
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…
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…
Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach
Zicheng Zhao, Linhao Luo, Shirui Pan +2
Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict missing links for unseen entities with few-shot links observed. Previous methods are limited to transdu…
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.
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
Xin Zheng, Miao Zhang, Chunyang Chen +3
Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has immediate benefits for various graph learni…
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…
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…
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…
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…
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…
Semi-decentralized Federated Ego Graph Learning for Recommendation
Liang Qu, Ningzhi Tang, Ruiqi Zheng +4
Collaborative filtering (CF) based recommender systems are typically trained based on personal interaction data (e.g., clicks and purchases) that could be naturally represented as…