23 citations · 48 across the 19 of their papers we have counts for
11 papers · 1 filter
Learning and Editing Universal Graph Prompt Tuning via Reinforcement Learning
Jinfeng Xu, Zheyu Chen, Shuo Yang +4
Early graph prompt tuning approaches relied on task-specific designs for Graph Neural Networks (GNNs), limiting their adaptability across diverse pre-training strategies. In contra…
VI-MMRec: Similarity-Aware Training Cost-free Virtual User-Item Interactions for Multimodal Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +6
Although existing multimodal recommendation models have shown promising performance, their effectiveness continues to be limited by the pervasive data sparsity problem. This proble…
Multi-modal Dynamic Proxy Learning for Personalized Multiple Clustering
Jinfeng Xu, Zheyu Chen, Shuo Yang +6
Multiple clustering aims to discover diverse latent structures from different perspectives, yet existing methods generate exhaustive clusterings without discerning user interest, n…
The Best is Yet to Come: Graph Convolution in the Testing Phase for Multimodal Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +2
The efficiency and scalability of graph convolution networks (GCNs) in training recommender systems remain critical challenges, hindering their practical deployment in real-world s…
RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking
Shuo Yang, Zijian Yu, Zhenzhe Ying +6
The rapid proliferation of multimodal misinformation presents significant challenges for automated fact-checking systems, especially when claims are ambiguous or lack sufficient co…
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +5
Graph Neural Networks (GNNs) are widely used in collaborative filtering to capture high-order user-item relationships. To address the data sparsity problem in recommendation system…
Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions
Shuo Yang, Xinran Zheng, Xinchen Zhang +5
Large Language Models (LLMs) have revolutionized various fields with their exceptional capabilities in understanding, processing, and generating human-like text. This paper investi…
Generative AI for Vulnerability Detection in 6G Wireless Networks: Advances, Case Study, and Future Directions
Shuo Yang, Xinran Zheng, Jinfeng Xu +4
The rapid advancement of 6G wireless networks, IoT, and edge computing has significantly expanded the cyberattack surface, necessitating more intelligent and adaptive vulnerability…
RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking
Shuo Yang, Yuqin Dai, Guoqing Wang +6
Large Language Models (LLMs) hold significant potential for advancing fact-checking by leveraging their capabilities in reasoning, evidence retrieval, and explanation generation. H…
MDVT: Enhancing Multimodal Recommendation with Model-Agnostic Multimodal-Driven Virtual Triplets
Jinfeng Xu, Zheyu Chen, Jinze Li +6
The data sparsity problem significantly hinders the performance of recommender systems, as traditional models rely on limited historical interactions to learn user preferences and…
A Survey on Multimodal Recommender Systems: Recent Advances and Future Directions
Jinfeng Xu, Zheyu Chen, Shuo Yang +5
Acquiring valuable data from the rapidly expanding information on the internet has become a significant concern, and recommender systems have emerged as a widely used and effective…