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Found 6 papers, 2 papers with code
Dual-domain Collaborative Denoising for Social Recommendation
The embedding-space collaborative denoising module devotes to resisting the noise cross-domain diffusion problem through contrastive learning with dual-domain embedding collaborative perturbation.
TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation
Specifically, the framework employs the SSEM at the bottom of the model to differentiate between samples, thereby assigning a more suitable encoder for each sample.
Ranked #1 on
Click-Through Rate Prediction
on Frappe
Generative-Enhanced Heterogeneous Graph Contrastive Learning
3) A hierarchical contrastive learning strategy for capturing local and global information.
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.
