86 citations · 125 across the 6 of their papers we have counts for
9 papers
IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search
Dian Cheng, Jiawei Chen, Wenjun Peng +5
A good personalized product search (PPS) system should not only focus on retrieving relevant products, but also consider user personalized preference. Recent work on PPS mainly ado…
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…
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
Junkang Wu, Wentao Shi, Xuezhi Cao +5
Knowledge graph completion (KGC) has become a focus of attention across deep learning community owing to its excellent contribution to numerous downstream tasks. Although recently…
AutoDebias: Learning to Debias for Recommendation
Jiawei Chen, Hande Dong, Yang Qiu +5
Recommender systems rely on user behavior data like ratings and clicks to build personalization model. However, the collected data is observational rather than experimental, causin…
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…