SVD-GCN
Author:
Affiliation:
1. Kyoto University, Kyoto, Japan
2. Kyushu University, Fukuoka, Japan
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3511808.3557462
Reference42 articles.
1. TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation
2. Rianne van den Berg , Thomas N Kipf , and Max Welling . 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 ( 2017 ). Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017).
3. Attentive Collaborative Filtering
4. Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
5. Graph Neural Networks for Social Recommendation
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1. FDGNN: Feature-Aware Disentangled Graph Neural Network for Recommendation;IEEE Transactions on Computational Social Systems;2024-02
2. GCN-based Explainable Recommendation using a Knowledge Graph and a Language Model;2023 IEEE International Conference on Big Data (BigData);2023-12-15
3. Enhancing Item-level Bundle Representation for Bundle Recommendation;ACM Transactions on Recommender Systems;2023-12-13
4. Self-supervised-Enhanced Dual Hierarchical Graph Convolution Network for Social Recommendation;Communications in Computer and Information Science;2023-11-26
5. Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21
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