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A two‐phase knowledge distillation model for graph convolutional network‐based recommendation

https://doi.org/10.1002/int.22819
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What this work says

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User Graph Regularized Pairwise Matrix Factorization for Item Recommendation
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inferredcites →https://doi.org/10.1109/lsp.2020.2975426openalex-snapshot
Integral Knowledge Distillation for Multi-Person Pose Estimation
inferredcites →https://doi.org/10.1109/tc.2020.2988006openalex-snapshot
Modularized Morphing of Deep Convolutional Neural Networks: A Graph Approach
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Tensor Graph Convolutional Networks for Multi-Relational and Robust Learning
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Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
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Modeling the Assimilation-Contrast Effects in Online Product Rating Systems
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inferredcites →https://doi.org/10.1609/aaai.v34i04.6094openalex-snapshot
Multi-Component Graph Convolutional Collaborative Filtering
inferredcites →https://doi.org/10.24963/ijcai.2017/239openalex-snapshot
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
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Deep Matrix Factorization Models for Recommender Systems
inferredcites →https://doi.org/10.48550/arxiv.2006.15516openalex-snapshot
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Graph Decipher: A transparent dual‐attention graph neural network to understand the message‐passing mechanism for the node classification
inferred← citeshttps://doi.org/10.1007/978-3-031-43427-3_36openalex-snapshot
Future Augmentation with Self-distillation in Recommendation
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inferred← citeshttps://doi.org/10.1016/j.neunet.2025.108061openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.neunet.2026.108674openalex-snapshot
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inferred← citeshttps://doi.org/10.1145/3568679openalex-snapshot
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DAG: Dual Attention Graph Representation Learning for Node Classification
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GCN-MF: A graph convolutional network based on matrix factorization for recommendation
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