Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open Challenges-Reference-Cited by-同舟云学术

Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open Challenges

Published:2023-10-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
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Author:

Purificato Erasmo¹^ORCID,Boratto Ludovico²^ORCID,De Luca Ernesto William³^ORCID

Affiliation:

1. Otto von Guericke University Magdeburg & Georg Eckert Institute, Magdeburg & Brunswick, Germany

2. University of Cagliari, Cagliari, Italy

3. Otto von Guericke University Magdeburg & Georg Eckert Institute, Magdeburg, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583780.3615292

Reference38 articles.

1. FairUP: A Framework for Fairness Analysis of Graph Neural Network-Based User Profiling Models

2. Transparent, Scrutable and Explainable User Models for Personalized Recommendation

3. Solon Barocas Moritz Hardt and Arvind Narayanan. 2019. Fairness and Machine Learning. fairmlbook.org. http://www.fairmlbook.org. Solon Barocas Moritz Hardt and Arvind Narayanan. 2019. Fairness and Machine Learning. fairmlbook.org. http://www.fairmlbook.org.

4. 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).

5. 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).