414 citations · 498 across the 3 of their papers we have counts for
3 papers
cs.IR2020★ 70 cited
How to Retrain Recommender System? A Sequential Meta-Learning Method
Yang Zhang, Fuli Feng, Chenxu Wang +4
Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the m…
cs.LG2020★ 14 cited
Bilinear Graph Neural Network with Neighbor Interactions
Hongmin Zhu, Fuli Feng, Xiangnan He +4
Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a…
cs.IR2020★ 414 cited
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Xiangnan He, Kuan Deng, Xiang Wang +3
Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understo…