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

Published: 19 June 2023 Publication History

Abstract

The proposed tutorial aims to introduce the UMAP community to modern user profiling approaches leveraging graph neural networks (GNNs). We will begin by discussing the conceptual foundations of user profiling and GNNs and providing a literature review of the two topics. We will then present a systematic overview of the state-of-the-art GNN architectures designed for user profiling, including the types of data that are typically used for this purpose. We will also discuss ethical considerations and beyond-accuracy perspectives (i.e. fairness and explainability), which can arise within the potential applications of adopting GNNs for user profiling. In the practical session of the tutorial, attendees will have the opportunity to understand concretely how recent GNN models for user profiling are built and trained with open-source tools and publicly available datasets. The audience will also be engaged in investigating the impact of the presented models on case studies involving bias detection and mitigation, as well as user profiles explanations. The tutorial will end with an analysis of existing and emerging open challenges in the field and their future research directions.

References

[1]
Krisztian Balog, Filip Radlinski, and Shushan Arakelyan. 2019. Transparent, scrutable and explainable user models for personalized recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 265–274.
[2]
Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2019. Fairness and Machine Learning. fairmlbook.org. http://www.fairmlbook.org.
[3]
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).
[5]
Weijian Chen, Yulong Gu, Zhaochun Ren, Xiangnan He, Hongtao Xie, Tong Guo, Dawei Yin, and Yongdong Zhang. 2019. Semi-supervised user profiling with heterogeneous graph attention networks. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2116–2122.
[6]
David Jaime Tena Cucala, Bernardo Cuenca Grau, Egor V Kostylev, and Boris Motik. 2021. Explainable GNN-Based Models over Knowledge Graphs. In International Conference on Learning Representations.
[7]
Enyan Dai and Suhang Wang. 2021. Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 680–688.
[8]
Christopher Ifeanyi Eke, Azah Anir Norman, Liyana Shuib, and Henry Friday Nweke. 2019. A survey of user profiling: State-of-the-art, challenges, and solutions. IEEE Access 7 (2019), 144907–144924.
[9]
European-Commission. 2019. Ethics guidelines for trustworthy AI. Publications Office. https://doi.org/doi/10.2759/346720
[10]
Elizabeth Gómez, Carlos Shui Zhang, Ludovico Boratto, Maria Salamó, and Mirko Marras. 2021. The winner takes it all: geographic imbalance and provider (un) fairness in educational recommender systems. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1808–1812.
[11]
Yulong Gu, Zhuoye Ding, Shuaiqiang Wang, and Dawei Yin. 2020. Hierarchical user profiling for e-commerce recommender systems. In Proceedings of the 13th International Conference on Web Search and Data Mining. 223–231.
[12]
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems 30 (2017).
[13]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 639–648.
[14]
Sumitkumar Kanoje, Sheetal Girase, and Debajyoti Mukhopadhyay. 2015. User profiling trends, techniques and applications. arXiv preprint arXiv:1503.07474 (2015).
[15]
Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Conference Track Proceedings.
[16]
Lizi Liao, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua. 2018. Attributed social network embedding. IEEE Transactions on Knowledge and Data Engineering 30, 12 (2018), 2257–2270.
[17]
Mohammad Naiseh, Nan Jiang, Jianbing Ma, and Raian Ali. 2020. Personalising explainable recommendations: Literature and conceptualisation. In World Conference on Information Systems and Technologies. Springer, 518–533.
[18]
Danny Poo, Brian Chng, and Jie-Mein Goh. 2003. A hybrid approach for user profiling. In 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the. IEEE, 9–13.
[19]
Erasmo Purificato, Ludovico Boratto, and Ernesto William De Luca. 2022. Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact and Mistreatment in Behavioural User Profiling. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 4399–4403.
[20]
Erasmo Purificato, Sabine Wehnert, and Ernesto William De Luca. 2021. Dynamic Privacy-Preserving Recommendations on Academic Graph Data. Computers 10, 9 (2021), 107.
[21]
Afshin Rahimi, Trevor Cohn, and Timothy Baldwin. 2018. Semi-supervised user geolocation via graph convolutional networks. arXiv preprint arXiv:1804.08049 (2018).
[22]
Tahleen Rahman, Bartlomiej Surma, Michael Backes, and Yang Zhang. 2019. Fairwalk: towards fair graph embedding. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 3289–3295.
[23]
Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, and Klaus-Robert Müller. 2019. Explainable AI: interpreting, explaining and visualizing deep learning. Vol. 11700. Springer Nature.
[24]
Zhiqing Sun, Jian Tang, Pan Du, Zhi-Hong Deng, and Jian-Yun Nie. 2019. Divgraphpointer: A graph pointer network for extracting diverse keyphrases. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 755–764.
[25]
Lubos Takac and Michal Zabovsky. 2012. Data analysis in public social networks. In International scientific conference and international workshop present day trends of innovations, Vol. 1. Present Day Trends of Innovations Lamza Poland.
[26]
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).
[27]
Qianwen Wang, Yao Ming, Zhihua Jin, Qiaomu Shen, Dongyu Liu, Micah J Smith, Kalyan Veeramachaneni, and Huamin Qu. 2019. Atmseer: Increasing transparency and controllability in automated machine learning. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–12.
[28]
Chuhan Wu, Fangzhao Wu, Junxin Liu, Shaojian He, Yongfeng Huang, and Xing Xie. 2019. Neural demographic prediction using search query. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. 654–662.
[29]
Qilong Yan, Yufeng Zhang, Qiang Liu, Shu Wu, and Liang Wang. 2021. Relation-aware Heterogeneous Graph for User Profiling. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3573–3577.
[30]
Liang Yao, Chengsheng Mao, and Yuan Luo. 2019. Graph convolutional networks for text classification. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 7370–7377.
[31]
Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. Gnn explainer: A tool for post-hoc explanation of graph neural networks. arXiv preprint arXiv:1903.03894 (2019).
[32]
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 974–983.
[33]
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. 2022. Explainability in graph neural networks: A taxonomic survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022).
[34]
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V Chawla. 2019. Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 793–803.
[35]
Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2022. Deep Learning on Graphs: A Survey. IEEE Transactions on Knowledge and Data Engineering 34, 1 (2022), 249–270.

Cited By

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  • (2024)Transparent Learner Knowledge State Modeling using Personal Knowledge Graphs and Graph Neural NetworksAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665230(591-596)Online publication date: 27-Jun-2024

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cover image ACM Conferences
UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
June 2023
333 pages
ISBN:9781450399326
DOI:10.1145/3565472
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 19 June 2023

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Author Tags

  1. Explainability
  2. Fairness
  3. Graph Neural Networks
  4. User Profiling

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View all
  • (2024)Transparent Learner Knowledge State Modeling using Personal Knowledge Graphs and Graph Neural NetworksAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665230(591-596)Online publication date: 27-Jun-2024

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