2.2k citations · 6k across the 49 of their papers we have counts for
86 papers
Unbiased Knowledge Distillation for Recommendation
Gang Chen, Jiawei Chen, Fuli Feng +2
As a promising solution for model compression, knowledge distillation (KD) has been applied in recommender systems (RS) to reduce inference latency. Traditional solutions first tra…
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…
GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation
Jiajia Chen, Xin Xin, Xianfeng Liang +2
Generating recommendations based on user-item interactions and user-user social relations is a common use case in web-based systems. These connections can be naturally represented…
Reinforced Causal Explainer for Graph Neural Networks
Xiang Wang, Yingxin Wu, An Zhang +3
Explainability is crucial for probing graph neural networks (GNNs), answering questions like "Why the GNN model makes a certain prediction?". Feature attribution is a prevalent tec…
Cross Pairwise Ranking for Unbiased Item Recommendation
Qi Wan, Xiangnan He, Xiang Wang +3
Most recommender systems optimize the model on observed interaction data, which is affected by the previous exposure mechanism and exhibits many biases like popularity bias. The lo…
Attention in Attention: Modeling Context Correlation for Efficient Video Classification
Yanbin Hao, Shuo Wang, Pei Cao +4
Attention mechanisms have significantly boosted the performance of video classification neural networks thanks to the utilization of perspective contexts. However, the current rese…