activity
20192026
most citedWhen Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities

338 citations · 354 across the 17 of their papers we have counts for

collaborators
Showing 2023Show all

5 papers · 1 filter

cs.CR2023★ 2 cited

Model Stealing Attack against Recommender System

Zhihao Zhu, Rui Fan, Chenwang Wu +3

Recent studies have demonstrated the vulnerability of recommender systems to data privacy attacks. However, research on the threat to model privacy in recommender systems, such as…

cs.LG2023

Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity

Zhihao Zhu, Chenwang Wu, Rui Fan +4

Recent research demonstrates that GNNs are vulnerable to the model stealing attack, a nefarious endeavor geared towards duplicating the target model via query permissions. However,…

cs.LG2023

Toward Robust Recommendation via Real-time Vicinal Defense

Yichang Xu, Chenwang Wu, Defu Lian

Recommender systems have been shown to be vulnerable to poisoning attacks, where malicious data is injected into the dataset to cause the recommender system to provide biased recom…

cs.IR2023★ 338 cited

When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities

Jin Chen, Zheng Liu, Xu Huang +9

The advent of large language models marks a revolutionary breakthrough in artificial intelligence. With the unprecedented scale of training and model parameters, the capability of…

cs.LG2023★ 5 cited

Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors

Moritz Neun, Christian Eichenberger, Henry Martin +27

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem…