2.2k citations · 3k across the 3 of their papers we have counts for
6 papers
Disentangled Graph Collaborative Filtering
Xiang Wang, Hongye Jin, An Zhang +3
Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user…
Reinforced Negative Sampling over Knowledge Graph for Recommendation
Xiang Wang, Yaokun Xu, Xiangnan He +3
Properly handling missing data is a fundamental challenge in recommendation. Most present works perform negative sampling from unobserved data to supply the training of recommender…
Unusual Metal to Marginal-Metal Transition in Two-Dimensional Ferromagnetic Electron Gases
Weiwei Chen, C. Wang, Qinwei Shi +2
Two-dimensional ferromagnetic electron gases subject to random scalar potentials and Rashba spin-orbit interactions exhibit a striking quantum criticality. As disorder strength …
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…
Neural Graph Collaborative Filtering
Xiang Wang, Xiangnan He, Meng Wang +2
Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged dee…
Item Silk Road: Recommending Items from Information Domains to Social Users
Xiang Wang, Xiangnan He, Liqiang Nie +1
Online platforms can be divided into information-oriented and social-oriented domains. The former refers to forums or E-commerce sites that emphasize user-item interactions, like T…