338 citations · 1.4k across the 188 of their papers we have counts for
5 papers · 1 filter
Sampling-Decomposable Generative Adversarial Recommender
Binbin Jin, Defu Lian, Zheng Liu +4
Recommendation techniques are important approaches for alleviating information overload. Being often trained on implicit user feedback, many recommenders suffer from the sparsity c…
Deep Pairwise Hashing for Cold-start Recommendation
Yan Zhang, Ivor W. Tsang, Hongzhi Yin +3
Recommendation efficiency and data sparsity problems have been regarded as two challenges of improving performance for online recommendation. Most of the previous related work focu…
Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation
Le Wu, Yonghui Yang, Lei Chen +3
With the increasing availability of videos, how to edit them and present the most interesting parts to users, i.e., video highlight, has become an urgent need with many broad appli…
GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions
Hanchen Wang, Defu Lian, Ying Zhang +2
Entity interaction prediction is essential in many important applications such as chemistry, biology, material science, and medical science. The problem becomes quite challenging w…
Binarized Graph Neural Network
Hanchen Wang, Defu Lian, Ying Zhang +4
Recently, there have been some breakthroughs in graph analysis by applying the graph neural networks (GNNs) following a neighborhood aggregation scheme, which demonstrate outstandi…