3.2k citations · 18.4k across the 142 of their papers we have counts for
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cs.LG2019★ 2.2k cited
KGAT: Knowledge Graph Attention Network for Recommendation
Xiang Wang, Xiangnan He, Yixin Cao +2
To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional…
cs.LG2019★ 26 cited
LambdaOpt: Learn to Regularize Recommender Models in Finer Levels
Yihong Chen, Bei Chen, Xiangnan He +4
Recommendation models mainly deal with categorical variables, such as user/item ID and attributes. Besides the high-cardinality issue, the interactions among such categorical varia…
cs.LG2019★ 29 cited
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Fuli Feng, Xiangnan He, Jie Tang +1
Recent efforts show that neural networks are vulnerable to small but intentional perturbations on input features in visual classification tasks. Due to the additional consideration…