68 citations · 203 across the 30 of their papers we have counts for
3 papers · 1 filter
Top-N Recommendation with Counterfactual User Preference Simulation
Mengyue Yang, Quanyu Dai, Zhenhua Dong +3
Top-N recommendation, which aims to learn user ranking-based preference, has long been a fundamental problem in a wide range of applications. Traditional models usually motivate th…
SimpleX: A Simple and Strong Baseline for Collaborative Filtering
Kelong Mao, Jieming Zhu, Jinpeng Wang +4
Collaborative filtering (CF) is a widely studied research topic in recommender systems. The learning of a CF model generally depends on three major components, namely interaction e…
Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs
Jiaren Xiao, Quanyu Dai, Xiaochen Xie +2
The high cost of data labeling often results in node label shortage in real applications. To improve node classification accuracy, graph-based semi-supervised learning leverages th…