2 citations · 5 across the 13 of their papers we have counts for
6 papers · 1 filter
Turning Semantics into Topology: LLM-Driven Attribute Augmentation for Collaborative Filtering
Junjie Meng, Ranxu zhang, Wei Wu +6
Large Language Models (LLMs) have shown great potential for enhancing recommender systems through their extensive world knowledge and reasoning capabilities. However, effectively t…
Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models
Rui Cai, Chao Wang, Qianyi Cai +2
Knowledge Graph-based recommendations have gained significant attention due to their ability to leverage rich semantic relationships. However, constructing and maintaining Knowledg…
Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation
Shengzhe Zhang, Liyi Chen, Dazhong Shen +2
Multi-modal sequential recommendation (SR) leverages multi-modal data to learn more comprehensive item features and user preferences than traditional SR methods, which has become a…
DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives
Leilei Ding, Dazhong Shen, Chao Wang +3
Graph Convolutional Networks (GCNs) have become pivotal in recommendation systems for learning user and item embeddings by leveraging the user-item interaction graph's node informa…
AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Wei Wu, Chao Wang, Dazhong Shen +3
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture colla…
Graph Signal Diffusion Model for Collaborative Filtering
Yunqin Zhu, Chao Wang, Qi Zhang +1
Collaborative filtering is a critical technique in recommender systems. It has been increasingly viewed as a conditional generative task for user feedback data, where newly develop…