43 citations · 121 across the 7 of their papers we have counts for
7 papers
Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
Zihao Zhao, Jiawei Chen, Sheng Zhou +4
Recommender system usually suffers from severe popularity bias -- the collected interaction data usually exhibits quite imbalanced or even long-tailed distribution over items. Such…
Distilling Holistic Knowledge with Graph Neural Networks
Sheng Zhou, Yucheng Wang, Defang Chen +4
Knowledge Distillation (KD) aims at transferring knowledge from a larger well-optimized teacher network to a smaller learnable student network.Existing KD methods have mainly consi…
Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation
Ning Ma, Jiajun Bu, Lixian Lu +4
Domain Adaptation has been widely used to deal with the distribution shift in vision, language, multimedia etc. Most domain adaptation methods learn domain-invariant features with…
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
Jiawei Chen, Chengquan Jiang, Can Wang +5
Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data. A typical strategy is to draw negative instanc…
SamWalker++: recommendation with informative sampling strategy
Can Wang, Jiawei Chen, Sheng Zhou +3
Recommendation from implicit feedback is a highly challenging task due to the lack of reliable negative feedback data. Existing methods address this challenge by treating all the u…
Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback
Jiawei Chen, Can Wang, Sheng Zhou +4
Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendat…