402 citations · 843 across the 16 of their papers we have counts for
7 papers · 1 filter
Scalable Graph Neural Networks via Bidirectional Propagation
Ming Chen, Zhewei Wei, Bolin Ding +4
Graph Neural Networks (GNN) is an emerging field for learning on non-Euclidean data. Recently, there has been increased interest in designing GNN that scales to large graphs. Most…
Contrastive Learning for Sequential Recommendation
Xu Xie, Fei Sun, Zhaoyang Liu +4
Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical interactio…
Simple and Deep Graph Convolutional Networks
Ming Chen, Zhewei Wei, Zengfeng Huang +2
Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in vario…
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Ruiyang Ren, Zhaoyang Liu, Yaliang Li +4
Recently, deep learning has made significant progress in the task of sequential recommendation. Existing neural sequential recommenders typically adopt a generative way trained wit…
Practical Data Poisoning Attack against Next-Item Recommendation
Hengtong Zhang, Yaliang Li, Bolin Ding +1
Online recommendation systems make use of a variety of information sources to provide users the items that users are potentially interested in. However, due to the openness of the…
Automated Relational Meta-learning
Huaxiu Yao, Xian Wu, Zhiqiang Tao +4
In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones. However, a critical challenge i…