3 papers
cs.IR2020
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
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
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…