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Found 4 papers, 1 papers with code
Generative-Contrastive Heterogeneous Graph Neural Network
In recent years, inspired by self-supervised learning, contrastive Heterogeneous Graphs Neural Networks (HGNNs) have shown great potential by utilizing data augmentation and discriminators for downstream tasks.
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation
Furthermore, we introduce a privacy-preserving decoder to mitigate user privacy leakage during knowledge transfer.
CETN: Contrast-enhanced Through Network for CTR Prediction
Click-through rate (CTR) Prediction is a crucial task in personalized information retrievals, such as industrial recommender systems, online advertising, and web search.
Ranked #4 on
Click-Through Rate Prediction
on Avazu
AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding
Existing methods usually adopt a "one-size-fits-all" approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in the embedding learning.
