Differential privacy in collaborative filtering recommender systems: a review
State-of-the-art recommender systems produce high-quality recommendations to support
users in finding relevant content. However, through the utilization of users' data for …
users in finding relevant content. However, through the utilization of users' data for …
Recruitpro: A pretrained language model with skill-aware prompt learning for intelligent recruitment
Recent years have witnessed the rapid development of machine-learning-based intelligent
recruitment services. Along this line, a large number of emerging models have been …
recruitment services. Along this line, a large number of emerging models have been …
Heterogeneous graph neural network for privacy-preserving recommendation
Social networks are considered to be heterogeneous graph neural networks (HGNNs) with
deep learning technological advances. HGNNs, compared to homogeneous data, absorb …
deep learning technological advances. HGNNs, compared to homogeneous data, absorb …
Adaptive Differentially Private Structural Entropy Minimization for Unsupervised Social Event Detection
Social event detection refers to extracting relevant message clusters from social media data
streams to represent specific events in the real world. Social event detection is important in …
streams to represent specific events in the real world. Social event detection is important in …
Poincaré Differential Privacy for Hierarchy-aware Graph Embedding
Hierarchy is an important and commonly observed topological property in real-world graphs
that indicate the relationships between supervisors and subordinates or the organizational …
that indicate the relationships between supervisors and subordinates or the organizational …
Survey and open problems in privacy-preserving knowledge graph: merging, query, representation, completion, and applications
Abstract Knowledge Graph (KG) has attracted more and more companies' attention for its
ability to connect different types of data in meaningful ways and support rich data services …
ability to connect different types of data in meaningful ways and support rich data services …
Multi-granularity attribute similarity model for user alignment across social platforms under pre-aligned data sparsity
Abstract Cross-platform User Alignment (UA) aims to identify accounts belonging to the
same individual across multiple social network platforms. This study seeks to enhance the …
same individual across multiple social network platforms. This study seeks to enhance the …
Exploring Cross-Site User Modeling without Cross-Site User Identity Linkage: A Case Study of Content Preference Prediction
Performing user modeling on two or more social media platforms collaboratively and
complementing each other (cross-site user modeling) has been a significant problem in the …
complementing each other (cross-site user modeling) has been a significant problem in the …
Differentially Private Multi-Label Learning Is Harder Than You'd Think
A Hannemann, B Friedl… - 2024 IEEE European …, 2024 - ieeexplore.ieee.org
Machine Learning is key in modern data analysis. However, privacy concerns related to
sharing sensitive data often hinder the full potential of Machine Learning. One of the popular …
sharing sensitive data often hinder the full potential of Machine Learning. One of the popular …
Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach
W Hu, H Fang - ACM Transactions on Knowledge Discovery from Data, 2024 - dl.acm.org
With increasing frequency of high-profile privacy breaches in various online platforms, users
are becoming more concerned about their privacy. And recommender system is the core …
are becoming more concerned about their privacy. And recommender system is the core …