Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Accuracy Perspectives-Reference-Cited by-同舟云学术

Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Accuracy Perspectives

Published:2023-06-18 Issue: Volume: Page:
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Container-title:Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
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Author:

Purificato Erasmo¹^ORCID,Boratto Ludovico²^ORCID,De Luca Ernesto William¹^ORCID

Affiliation:

1. Otto von Guericke University Magdeburg, Germany

2. University of Cagliari, Italy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3565472.3595616

Reference35 articles.

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3. Simon Caton and Christian Haas . 2020. Fairness in machine learning: A survey. arXiv preprint arXiv:2010.04053 ( 2020 ). Simon Caton and Christian Haas. 2020. Fairness in machine learning: A survey. arXiv preprint arXiv:2010.04053 (2020).

4. Weijian Chen , Fuli Feng , Qifan Wang , Xiangnan He , Chonggang Song , Guohui Ling , and Yongdong Zhang . 2021. CatGCN: Graph Convolutional Networks with Categorical Node Features . IEEE Transactions on Knowledge and Data Engineering ( 2021 ). Weijian Chen, Fuli Feng, Qifan Wang, Xiangnan He, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021. CatGCN: Graph Convolutional Networks with Categorical Node Features. IEEE Transactions on Knowledge and Data Engineering (2021).

5. Semi-supervised User Profiling with Heterogeneous Graph Attention Networks

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