activity
20212025
most citedA Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

41 citations · 41 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2025

SynapseRoute: An Auto-Route Switching Framework on Dual-State Large Language Model

Wencheng Zhang, Shiqin Qiao, Lingjie Luo +9

With the widespread adoption of large language models (LLMs) in practical applications, selecting an appropriate model requires balancing not only performance but also operational…

cs.AI2024

ELASTIC: Efficient Linear Attention for Sequential Interest Compression

Jiaxin Deng, Shiyao Wang, Song Lu +5

State-of-the-art sequential recommendation models heavily rely on transformer's attention mechanism. However, the quadratic computational and memory complexities of self attention…

cs.IR2023

Inverse Learning with Extremely Sparse Feedback for Recommendation

Guanyu Lin, Chen Gao, Yu Zheng +8

Modern personalized recommendation services often rely on user feedback, either explicit or implicit, to improve the quality of services. Explicit feedback refers to behaviors like…

cs.LG2022

DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction

Yinfeng Li, Chen Gao, Quanming Yao +3

Spatiotemporal activity prediction, aiming to predict user activities at a specific location and time, is crucial for applications like urban planning and mobile advertising. Exist…

cs.IR2021★ 41 cited

A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

Chen Gao, Yu Zheng, Nian Li +8

Recommender system is one of the most important information services on today's Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommend…