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Recent advancements in Graph Neural Networks (GNN) have facilitated their widespread adoption in various applications, including recommendation systems.
DeepGCNs: Can GCNs Go as Deep as CNNs?
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Amazon. com recommendations: Item-to-item collaborative filtering
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On the Bottleneck of Graph Neural Networks and its Practical Implications
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Matrix Factorization Techniques for Recommender Systems
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Comparison of Collaborative Filtering Algorithms: Limitations of Current Techniques and Proposals for Scalable, High-Performance Recommender Systems
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BPR: Bayesian Personalized Ranking from Implicit Feedback
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SVD-based incremental approaches for recommender systems
Xun Zhou, Jing He, Guangyan Huang, and Yanchun Zhang. 2015 · 2014
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Matrix factorization model in collaborative filtering algorithms: A survey
Dheeraj Bokde, Sheetal Girase, and Debajyoti Mukhopadhyay. 2015 · 2015
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Convolutional Networks on Graphs for Learning Molecular Fingerprints. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 (Montreal, Canada) (NIPS’15) . MIT Press, Cambridge, MA, USA, 2224–2232
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Modeling User Exposure in Recommendation
Dawen Liang, Laurent Charlin, James McInerney, and David M Blei. 2015 · 2015
Cited alongside, same era.
AutoRec: Autoencoders Meet Collaborative Filtering. In Proceedings of the 24th International Conference on World Wide Web (Florence, Italy) (WWW ’15 Companion) . Association for Computing Machinery, New York, NY, USA, 111–112
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Toward a spectral theory of cellular sheaves
Jakob Hansen and Robert Ghrist. 2019 · 2019
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Deep Learning Based Recommender System
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019b · 2019
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EnsVAE: Ensemble Variational Autoencoders for Recommendations
Ahlem Drif, Houssem Eddine Zerrad, and Hocine Cherifi. 2020 · 2020
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LightGCN
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
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A Comprehensive Survey on Graph Neural Networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2021 · 2020
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Graph neural networks for friend ranking in large-scale social platforms. In Proceedings of the Web Conference 2021 . 2535–2546
Aravind Sankar, Yozen Liu, Jun Yu, and Neil Shah. 2021 · 2021
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling. 2016 · 2016
Cited alongside, same era.
Social Collaborative Filtering by Trust
Bo Yang, Yu Lei, Jiming Liu, and Wenjie Li. 2017 · 2016
Cited alongside, same era.
A Neural Autoregressive Approach to Collaborative Filtering
Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou. 2016 · 2016
Cited alongside, same era.
Neural Collaborative Filtering
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
Training deep autoencoders for collaborative filtering
Oleksii Kuchaiev and Boris Ginsburg. 2017 · 2017
Cited alongside, same era.
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Copenhagen, Denmark, 1506–1515
Diego Marcheggiani and Ivan Titov. 2017 · 2017
Cited alongside, same era.
Graph Convolutional Matrix Completion
Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017 · 2017
Cited alongside, same era.
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
Later among the works it cites.
Sheaf Neural Networks with Connection Laplacians
Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael Bronstein, Petar Veličković, and Pietro Liò. 2022 · 2022
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Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Liò, and Michael M. Bronstein. 2022 · 2022
Later among the works it cites.
Graph neural networks in recommender systems: a survey
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui. 2022 · 2022
Later among the works it cites.
Learning to Solve PDE-constrained Inverse Problems with Graph Networks
Qingqing Zhao, David B. Lindell, and Gordon Wetzstein. 2022 · 2022
Later among the works it cites.
Iulia Duta, Giulia Cassarà, Fabrizio Silvestri, and Pietro Liò. 2023 · 2023
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KGFlex: Efficient Recommendation with Sparse Feature Factorization and Knowledge Graphs
Antonio Ferrara, Vito Walter Anelli, Alberto Carlo Maria Mancino, Tommaso Di Noia, and Eugenio Di Sciascio. 2023 · 2023
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A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He, and Yong Li. 2023 · 2023
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KGTORe: Tailored Recommendations through Knowledge-Aware GNN Models. In Proceedings of the 17th ACM Conference on Recommender Systems (Singapore, Singapore) (RecSys ’23) . Association for Computing Machinery, New York, NY, USA, 576–587
Alberto Carlo Maria Mancino, Antonio Ferrara, Salvatore Bufi, Daniele Malitesta, Tommaso Di Noia, and Eugenio Di Sciascio. 2023 · 2023
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UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, and Xiuqiang He. 2023 · 2023
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