23 citations · 57 across the 22 of their papers we have counts for
12 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…
Spec-LLaVA: Accelerating Vision-Language Models with Dynamic Tree-Based Speculative Decoding
Mingxiao Huo, Jiayi Zhang, Hewei Wang +4
Vision-Language Models (VLMs) enable powerful multimodal reasoning but suffer from slow autoregressive inference, limiting their deployment in real-time applications. We introduce…
Hypercomplex Prompt-aware Multimodal Recommendation
Zheyu Chen, Jinfeng Xu, Hewei Wang +3
Modern recommender systems face critical challenges in handling information overload while addressing the inherent limitations of multimodal representation learning. Existing metho…
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…
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…
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…
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…
COHESION: Composite Graph Convolutional Network with Dual-Stage Fusion for Multimodal Recommendation
Jinfeng Xu, Zheyu Chen, Wei Wang +3
Recent works in multimodal recommendations, which leverage diverse modal information to address data sparsity and enhance recommendation accuracy, have garnered considerable intere…
Squeeze and Excitation: A Weighted Graph Contrastive Learning for Collaborative Filtering
Zheyu Chen, Jinfeng Xu, Yutong Wei +1
Contrastive Learning (CL) has recently emerged as a powerful technique in recommendation systems, particularly for its capability to harness self-supervised signals from perturbed…
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…