338 citations · 454 across the 70 of their papers we have counts for
8 papers · 1 filter
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…
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,…
Large-Scale OD Matrix Estimation with A Deep Learning Method
Zheli Xiong, Defu Lian, Enhong Chen +2
The estimation of origin-destination (OD) matrices is a crucial aspect of Intelligent Transport Systems (ITS). It involves adjusting an initial OD matrix by regressing the current…
Interactive Graph Convolutional Filtering
Jin Zhang, Defu Lian, Hong Xie +2
Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. However,…
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification
Likang Wu, Junji Jiang, Hongke Zhao +4
Recently, Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. This task aims to predict nodes from unseen classes which are unobserved…
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…
A DeepLearning Framework for Dynamic Estimation of Origin-Destination Sequence
Zheli Xiong, Defu Lian, Enhong Chen +2
OD matrix estimation is a critical problem in the transportation domain. The principle method uses the traffic sensor measured information such as traffic counts to estimate the tr…
GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation
Yongqiang Han, Likang Wu, Hao Wang +5
Sequential Recommendation is a widely studied paradigm for learning users' dynamic interests from historical interactions for predicting the next potential item. Although lots of r…