activity
20212024
most citedAre Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation

83 citations · 117 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CL2024

Debate on Graph: a Flexible and Reliable Reasoning Framework for Large Language Models

Jie Ma, Zhitao Gao, Qi Chai +8

Large Language Models (LLMs) may suffer from hallucinations in real-world applications due to the lack of relevant knowledge. In contrast, knowledge graphs encompass extensive, mul…

cs.LG2024

Physics-guided Active Sample Reweighting for Urban Flow Prediction

Wei Jiang, Tong Chen, Guanhua Ye +4

Urban flow prediction is a spatio-temporal modeling task that estimates the throughput of transportation services like buses, taxis, and ride-sharing, where data-driven models have…

cs.IR20242 cited

Lightweight Embeddings for Graph Collaborative Filtering

Xurong Liang, Tong Chen, Lizhen Cui +3

Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods. Meanwhile, owing to the use of an embedding table to represent each user/item…

cs.IR20241 cited

Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation

Liang Qu, Wei Yuan, Ruiqi Zheng +3

Federated recommender systems (FedRecs) have gained significant attention for their potential to protect user's privacy by keeping user privacy data locally and only communicating…

cs.IR2024

Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation

Ruiqi Zheng, Liang Qu, Tong Chen +3

In Location-based Social Networks, Point-of-Interest (POI) recommendation helps users discover interesting places. There is a trend to move from the cloud-based model to on-device…

cs.CR202313 cited

An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Xilie Xu, Keyi Kong, Ning Liu +4

The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM's adversarial robustness. This pa…