60 citations · 90 across the 4 of their papers we have counts for
5 papers
MixDec Sampling: A Soft Link-based Sampling Method of Graph Neural Network for Recommendation
Xiangjin Xie, Yuxin Chen, Ruipeng Wang +8
Graph neural networks have been widely used in recent recommender systems, where negative sampling plays an important role. Existing negative sampling methods restrict the relation…
One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation
Chenglin Li, Yuanzhen Xie, Chenyun Yu +5
Cross-domain recommendation is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existing tech…
An Attention-based Graph Neural Network for Heterogeneous Structural Learning
Huiting Hong, Hantao Guo, Yucheng Lin +3
In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations.…
Interpretable Text Classification Using CNN and Max-pooling
Hao Cheng, Xiaoqing Yang, Zang Li +2
Deep neural networks have been widely used in text classification. However, it is hard to interpret the neural models due to the complicate mechanisms. In this work, we study the i…
AHINE: Adaptive Heterogeneous Information Network Embedding
Yucheng Lin, Xiaoqing Yang, Zang Li +1
Network embedding is an effective way to solve the network analytics problems such as node classification, link prediction, etc. It represents network elements using low dimensiona…