215 citations · 498 across the 15 of their papers we have counts for
4 papers · 1 filter
LLMRec: Large Language Models with Graph Augmentation for Recommendation
Wei Wei, Xubin Ren, Jiabin Tang +6
The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. Howev…
Representation Learning with Large Language Models for Recommendation
Xubin Ren, Wei Wei, Lianghao Xia +5
Recommender systems have seen significant advancements with the influence of deep learning and graph neural networks, particularly in capturing complex user-item relationships. How…
GraphGPT: Graph Instruction Tuning for Large Language Models
Jiabin Tang, Yuhao Yang, Wei Wei +5
Graph Neural Networks (GNNs) have evolved to understand graph structures through recursive exchanges and aggregations among nodes. To enhance robustness, self-supervised learning (…
Automated Self-Supervised Learning for Recommendation
Lianghao Xia, Chao Huang, Chunzhen Huang +3
Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contra…