activity
20182026
most citedMetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation

110 citations · 489 across the 29 of their papers we have counts for

collaborators

29 papers

cs.LG2026

SqLinear: Balanced Square Partitioning Makes Linear Interaction Sufficient for Large-Scale Traffic Forecasting

Yongfeng Su, Hongwen Li, Zijian Zhang +5

Traffic prediction is a core task in intelligent transportation systems and urban-scale decision making. Despite the effectiveness of mainstream neural network-based methods, their…

cs.DB2023

MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching

Xiaocan Zeng, Pengfei Wang, Yuren Mao +3

Entity Matching (EM), which aims to identify all entity pairs referring to the same real-world entity from relational tables, is one of the most important tasks in real-world data…

cs.CL2023★ 28 cited

C3: Zero-shot Text-to-SQL with ChatGPT

Xuemei Dong, Chao Zhang, Yuhang Ge +5

This paper proposes a ChatGPT-based zero-shot Text-to-SQL method, dubbed C3, which achieves 82.3\% in terms of execution accuracy on the holdout test set of Spider and becomes the…

cs.DB2023★ 1 cited

Real-time Workload Pattern Analysis for Large-scale Cloud Databases

Jiaqi Wang, Tianyi Li, Anni Wang +7

Hosting database services on cloud systems has become a common practice. This has led to the increasing volume of database workloads, which provides the opportunity for pattern ana…

cs.DB2023★ 2 cited

Modern Data Pricing Models: Taxonomy and Comprehensive Survey

Xiaoye Miao, Huanhuan Peng, Xinyu Huang +3

Data play an increasingly important role in smart data analytics, which facilitate many data-driven applications. The goal of various data markets aims to alleviate the issue of is…

cs.IR2023★ 35 cited

Knowledge-refined Denoising Network for Robust Recommendation

Xinjun Zhu, Yuntao Du, Yuren Mao +3

Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability. However, existing know…