Debiasing neighbor aggregation for graph neural network in recommender systems

M Kim, J Oh, J Do, S Lee - Proceedings of the 31st ACM International …, 2022 - dl.acm.org
Graph neural networks (GNNs) have achieved remarkable success in recommender
systems by representing users and items based on their historical interactions. However …
被引用次数：7 相关文章

Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems

M Kim, J Oh, J Do, S Lee - arXiv e-prints, 2022 - ui.adsabs.harvard.edu
Graph neural networks (GNNs) have achieved remarkable success in recommender
systems by representing users and items based on their historical interactions. However …

Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems

M Kim, J Oh, J Do, S Lee - arXiv preprint arXiv:2208.08847, 2022 - arxiv.org
Graph neural networks (GNNs) have achieved remarkable success in recommender
systems by representing users and items based on their historical interactions. However …

Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems

MS Kim, J Oh, JY Do, SJ Lee - Proceedings of the 31st ACM …, 2022 - s-space.snu.ac.kr
Graph neural networks (GNNs) have achieved remarkable success in recommender
systems by representing users and items based on their historical interactions. However …
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[HTML][HTML] Debiasing neighbor aggregation for graph neural network in recommender systems

M Kim, J Oh, J Do, S Lee - 2022 - amazon.science
Graph neural networks (GNNs) have achieved remarkable success in recommender
systems by representing users and items based on their historical interactions. However …
[HTML] amazon.science

[HTML][HTML] Debiasing neighbor aggregation for graph neural network in recommender systems

M Kim, J Oh, J Do, S Lee, B Omidvar-Tehrani, G Guinet… - amazon.science
Graph neural networks (GNNs) have achieved remarkable success in recommender
systems by representing users and items based on their historical interactions. However …