3 citations · 4 across the 5 of their papers we have counts for
6 papers
Task-Aware Retrieval Augmentation for Dynamic Recommendation
Zhen Tao, Xinke Jiang, Qingshuai Feng +6
Dynamic recommendation systems aim to provide personalized suggestions by modeling temporal user-item interactions across time-series behavioral data. Recent studies have leveraged…
STRAP: Spatio-Temporal Pattern Retrieval for Out-of-Distribution Generalization
Haoyu Zhang, Wentao Zhang, Hao Miao +3
Spatio-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool for modeling dynamic graph-structured data across diverse domains. However, they often fail to genera…
RAGraph: A General Retrieval-Augmented Graph Learning Framework
Xinke Jiang, Rihong Qiu, Yongxin Xu +7
Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs…
TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systems
Xinke Jiang, Yue Fang, Rihong Qiu +10
In the pursuit of enhancing domain-specific Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) emerges as a promising solution to mitigate issues such as hallucinat…
Time Series Supplier Allocation via Deep Black-Litterman Model
Jiayuan Luo, Wentao Zhang, Yuchen Fang +4
Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy order demands with maximum supply efficie…
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation
Ruizhe Zhang, Xinke Jiang, Yuchen Fang +6
Graph Neural Networks (GNNs) have shown considerable effectiveness in a variety of graph learning tasks, particularly those based on the message-passing approach in recent years. H…