37 citations · 68 across the 6 of their papers we have counts for
6 papers
Semantic-Enhanced Relational Metric Learning for Recommender Systems
Mingming Li, Fuqing Zhu, Feng Yuan +1
Recently, relational metric learning methods have been received great attention in recommendation community, which is inspired by the translation mechanism in knowledge graph. Diff…
MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation
Manqing Dong, Feng Yuan, Lina Yao +2
A common challenge for most current recommender systems is the cold-start problem. Due to the lack of user-item interactions, the fine-tuned recommender systems are unable to handl…
Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
Manqing Dong, Feng Yuan, Lina Yao +3
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation r…
DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns
Feng Yuan, Lina Yao, Boualem Benatallah
Cross-domain recommendation has long been one of the major topics in recommender systems. Recently, various deep models have been proposed to transfer the learned knowledge across…
Adversarial Variational Embedding for Robust Semi-supervised Learning
Xiang Zhang, Lina Yao, Feng Yuan
Semi-supervised learning is sought for leveraging the unlabelled data when labelled data is difficult or expensive to acquire. Deep generative models (e.g., Variational Autoencoder…
Adversarial Collaborative Auto-encoder for Top-N Recommendation
Feng Yuan, Lina Yao, Boualem Benatallah
During the past decade, model-based recommendation methods have evolved from latent factor models to neural network-based models. Most of these techniques mainly focus on improving…