56 citations · 109 across the 3 of their papers we have counts for
3 papers
cs.IR2022★ 53 cited
MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation
Kang Liu, Feng Xue, Dan Guo +3
In most E-commerce platforms, whether the displayed items trigger the user's interest largely depends on their most eye-catching multimodal content. Consequently, increasing effort…
cs.IR2022★ 56 cited
Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation
Kang Liu, Feng Xue, Xiangnan He +2
Graph Convolution Networks (GCNs), with their efficient ability to capture high-order connectivity in graphs, have been widely applied in recommender systems. Stacking multiple nei…
cs.LG2020
RGCF: Refined Graph Convolution Collaborative Filtering with concise and expressive embedding
Kang Liu, Feng Xue, Richang Hong
Graph Convolution Network (GCN) has attracted significant attention and become the most popular method for learning graph representations. In recent years, many efforts have been f…