ABSTRACT
The presented doctoral research aims to develop a behavioural user profiling framework focusing simultaneously on three beyond-accuracy perspectives: privacy, to study how to intervene on graph data structures of specific contexts and provide methods to make the data available in a meaningful manner without neither exposing personal user information nor corrupting the profiles creation and system performances; fairness, to provide user representations that are free of any inherited discrimination which could affect a downstream recommender by developing debiasing approaches to be applied on state-of-the-art GNN-based user profiling models; explainability, to produce understandable descriptions of the framework results, both for user profiles and recommendations, mainly in terms of interaction importance, by designing an adaptive and personalised user interface which provides tailored explanations to the end-users, depending on their specific user profiles.
Supplemental Material
- Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y Lim, and Mohan Kankanhalli. 2018. Trends and trajectories for explainable, accountable and intelligible systems: An hci research agenda. In Proceedings of the 2018 CHI conference on human factors in computing systems. 1–18.Google ScholarDigital Library
- Erfan Aghasian, Saurabh Garg, and James Montgomery. 2018. User’s Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy. In Big Data Recommender Systems: Recent Trends and Advances. The Institution of Engineering and Technology, 1–26.Google Scholar
- Sören Auer, Markus Stocker, Lars Vogt, Grischa Fraumann, and Alexandra Garatzogianni. 2021. ORKG: Facilitating the Transfer of Research Results with the Open Research Knowledge Graph. Research Ideas and Outcomes 7 (2021), e68513.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2019. Fairness and Machine Learning. fairmlbook.org. http://www.fairmlbook.org.Google Scholar
- Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth. 2021. Fairness in criminal justice risk assessments: The state of the art. Sociological Methods & Research 50, 1 (2021), 3–44.Google ScholarCross Ref
- Simon Caton and Christian Haas. 2020. Fairness in machine learning: A survey. arXiv preprint arXiv:2010.04053(2020).Google Scholar
- Chen, Feng, Wang, He, Song, Ling, and Zhang. 2021. CatGCN: Graph Convolutional Networks with Categorical Node Features. IEEE Trans. Knowl. Data Eng. (Dec. 2021), 1–1.Google Scholar
- Hyeoncheol Cho, Eok Kyun Lee, and Insung S Choi. 2020. Layer-wise relevance propagation of InteractionNet explains protein–ligand interactions at the atom level. Scientific reports 10, 1 (2020), 1–11.Google Scholar
- 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.Google ScholarDigital Library
- Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. 2015. Certifying and removing disparate impact. In proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 259–268.Google ScholarDigital Library
- Fatih Gedikli, Dietmar Jannach, and Mouzhi Ge. 2014. How should I explain? A comparison of different explanation types for recommender systems. International Journal of Human-Computer Studies 72, 4 (2014), 367–382.Google ScholarDigital Library
- David Gunning. 2017. Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA) 2 (2017).Google Scholar
- Sara Hajian, Francesco Bonchi, and Carlos Castillo. 2016. Algorithmic bias: From discrimination discovery to fairness-aware data mining. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2125–2126.Google ScholarDigital Library
- Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of opportunity in supervised learning. Advances in neural information processing systems 29 (2016).Google Scholar
- 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.Google ScholarDigital Library
- Sumitkumar Kanoje, Sheetal Girase, and Debajyoti Mukhopadhyay. 2015. User profiling trends, techniques and applications. arXiv preprint arXiv:1503.07474(2015).Google Scholar
- Shyong K Lam, Dan Frankowski, and John Riedl. 2006. Do You Trust Your Recommendations? An Exploration of Security and Privacy Issues in Recommender Systems. In Internation Conference on Emerging Trends in Information and Communication Security. Springer Berlin Heidelberg, 14–29.Google Scholar
- 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.Google ScholarDigital Library
- Martijn Millecamp, Nyi Nyi Htun, Cristina Conati, and Katrien Verbert. 2019. To explain or not to explain: the effects of personal characteristics when explaining music recommendations. In Proceedings of the 24th International Conference on Intelligent User Interfaces. Association for Computing Machinery, Los Angeles, CA, USA, 397–407.Google ScholarDigital Library
- Tim Miller. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence 267 (Feb. 2019), 1–38.Google Scholar
- Cataldo Musto, Fedelucio Narducci, Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. 2019. Linked open data-based explanations for transparent recommender systems. International Journal of Human-Computer Studies 121 (2019), 93–107.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Data Protection Focus Group of the Digital Summit. 2018. Requirements for the use of pseudonymisation solutions in compliance with data protection regulations. Technical Report. German Society for Data Protection and Data Security, Heinrich-Böll-Ring 10, 53119 Bonn, Germany. 17 pages. A working paper of the Data Protection Focus Group of the Platform Security, Protection and Trust for Society and Business, 2018.Google Scholar
- 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.Google ScholarCross Ref
- Erasmo Purificato, Baalakrishnan Aiyer Manikandan, Prasanth Vaidya Karanam, Mahantesh Vishvanath Pattadkal, and Ernesto William De Luca. 2021. Evaluating Explainable Interfaces for a Knowledge Graph-Based Recommender System. In Proceedings of the 8th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, co-located with RecSys’21 (Amsterdam, Netherlands). 73–88.Google Scholar
- Erasmo Purificato, Cataldo Musto, Pasquale Lops, and Ernesto William De Luca. 2022. First Workshop on Adaptive and Personalized Explainable User Interfaces (APEx-UI 2022). In 27th International Conference on Intelligent User Interfaces (Helsinki, Finland) (IUI ’22 Companion). Association for Computing Machinery, New York, NY, USA, 1–3. https://doi.org/10.1145/3490100.3511168Google ScholarDigital Library
- Erasmo Purificato, Sabine Wehnert, and Ernesto William De Luca. 2021. Dynamic Privacy-Preserving Recommendations on Academic Graph Data. Computers 10, 9 (2021), 107.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarDigital Library
- Mingyang Wan, Daochen Zha, Ninghao Liu, and Na Zou. 2021. Modeling Techniques for Machine Learning Fairness: A Survey. arXiv preprint arXiv:2111.03015(2021).Google Scholar
- Clarice Wang, Kathryn Wang, Andrew Bian, Rashidul Islam, Kamrun Naher Keya, James Foulds, and Shimei Pan. 2022. Do Humans Prefer Debiased AI Algorithms? A Case Study in Career Recommendation. In 27th International Conference on Intelligent User Interfaces (Helsinki, Finland) (IUI ’22). Association for Computing Machinery, New York, NY, USA, 134–147. https://doi.org/10.1145/3490099.3511108Google ScholarDigital Library
- 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. Association for Computing Machinery, New York, NY, USA, 3573–3577.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. Gnn explainer: A tool for post-hoc explanation of graph neural networks. (2019).Google Scholar
- Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi. 2017. Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th international conference on world wide web. 1171–1180.Google ScholarDigital Library
- Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2020. Graph neural networks: A review of methods and applications. AI Open 1(2020), 57–81.Google ScholarCross Ref
Index Terms
- Beyond-Accuracy Perspectives on Graph Neural Network-Based Models for Behavioural User Profiling
Recommendations
Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Accuracy Perspectives
UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and PersonalizationThe 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 ...
Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact and Mistreatment in Behavioural User Profiling
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementRecent approaches to behavioural user profiling employ Graph Neural Networks (GNNs) to turn users' interactions with a platform into actionable knowledge. The effectiveness of an approach is usually assessed with accuracy-based perspectives, where the ...
Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open Challenges
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge ManagementThe proposed tutorial aims to familiarise the CIKM community with modern user profiling techniques that utilise Graph Neural Networks (GNNs). Initially, we will delve into the foundational principles of user profiling and GNNs, accompanied by an overview ...
Comments