KGAT
To provide more accurate, diverse, and explainable recommendation, it is\ncompulsory to go beyond modeling user-item interactions and take side\ninformation into account. Tradit...
Publications
5 shownNeural Collaborative Filtering
In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neura...
Learning Image and User Features for Recommendation in Social Networks
Good representations of data do help in many machine learning tasks such as recommendation. It is often a great challenge for traditional recommender systems to learn representa...
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of exis...
Meta-Transfer Learning for Few-Shot Learning
Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in orde...
Frequent Co-Authors
Researcher Info
- h-index
- 5
- Publications
- 5
- Citations
- 10,535
- Institution
- National University of Singapore
External Links
Identifiers
- ORCID
- 0000-0001-6097-7807
Impact Metrics
h-index
5
h-index: Number of publications with at least h citations each.