Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends
Pages 13 - 16
Abstract
The presented tutorial aims to serve as a comprehensive roadmap for the UMAP community into the current user modeling research, focusing on the paradigm shifts that have transformed the research landscape in recent times. We will provide a complete overview of the large, long-standing, and ever-growing research fields of user modeling and user profiling, both from a historical and a technical point of view. We will then examine the definitions associated with each key term in this research domain, aiming to eliminate ambiguity and confusion in their usage. As the core of our tutorial, we present in-depth the paradigm shifts that have occurred in recent years, especially due to technological evolution, as well as the current research directions and novel trends in the field. In particular, we illustrate and discuss the advances in the following topics: implicit and explicit user profiling, user behavior modeling, user representation, and beyond-accuracy perspectives. The audience will be engaged in discussions during the whole presentation to foster the development of an interactive event.
Detailed information and resources about the tutorial are available on the website: https://link.erasmopurif.com/tutorial-umap24.
References
[1]
Mohamed Abdelrazek, Erasmo Purificato, Ludovico Boratto, and Ernesto William De Luca. 2023. FairUP: A Framework for Fairness Analysis of Graph Neural Network-Based User Profiling Models. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR ’23). Association for Computing Machinery, New York, NY, USA, 3165–3169. https://doi.org/10.1145/3539618.3591814
[2]
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. ACM, Paris France, 265–274. https://doi.org/10.1145/3331184.3331211
[3]
Peter Brusilovski, Alfred Kobsa, and Wolfgang Nejdl. 2007. The Adaptive Web: Methods and Strategies of Web Personalization. Springer Science & Business Media.
[4]
Peter Brusilovsky, Alfred Kobsa, and Julita Vassileva (Eds.). 1998. Adaptive Hypertext and Hypermedia. Springer Netherlands, Dordrecht. https://link.springer.com/10.1007/978-94-017-0617-9
[5]
Zhiyong Cheng, Sai Han, Fan Liu, Lei Zhu, Zan Gao, and Yuxin Peng. 2023. Multi-Behavior Recommendation with Cascading Graph Convolution Networks. In Proceedings of the ACM Web Conference 2023(WWW ’23). Association for Computing Machinery, New York, NY, USA, 1181–1189. https://doi.org/10.1145/3543507.3583439
[6]
Junsu Cho, Dongmin Hyun, Dong won Lim, Hyeon jae Cheon, Hyoung-iel Park, and Hwanjo Yu. 2023. Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence 37, 4 (June 2023), 4199–4207. https://doi.org/10.1609/aaai.v37i4.25537
[7]
Nick Craswell, Arjen de Vries, and Ian Soboroff. 2005. Overview of the TREC-2005 enterprise track.
[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. https://doi.org/10.1109/ACCESS.2019.2944243
[9]
Min Gao, Kecheng Liu, and Zhongfu Wu. 2010. Personalisation in web computing and informatics: Theories, techniques, applications, and future research. Information Systems Frontiers 12, 5 (Nov. 2010), 607–629. https://doi.org/10.1007/s10796-009-9199-3
[10]
Daniela Godoy and Analia Amandi. 2005. User profiling in personal information agents: a survey. The Knowledge Engineering Review 20, 4 (Dec. 2005), 329–361. https://doi.org/10.1017/S0269888906000397
[11]
Xiaowen Huang, Quan Fang, Shengsheng Qian, Jitao Sang, Yan Li, and Changsheng Xu. 2019. Explainable Interaction-driven User Modeling over Knowledge Graph for Sequential Recommendation. In Proceedings of the 27th ACM International Conference on Multimedia(MM ’19). Association for Computing Machinery, New York, NY, USA, 548–556. https://doi.org/10.1145/3343031.3350893
[12]
Sumitkumar Kanoje, Sheetal Girase, and Debajyoti Mukhopadhyay. 2015. User Profiling Trends, Techniques and Applications. https://doi.org/10.48550/arXiv.1503.07474
[13]
Sein Kim, Namkyeong Lee, Donghyun Kim, Minchul Yang, and Chanyoung Park. 2023. Task Relation-aware Continual User Representation Learning. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD ’23). Association for Computing Machinery, New York, NY, USA, 1107–1119. https://doi.org/10.1145/3580305.3599516
[14]
Alfred Kobsa. 2001. Generic User Modeling Systems. User Modeling and User-Adapted Interaction 11, 1 (March 2001), 49–63. https://doi.org/10.1023/A:1011187500863
[15]
Sichun Luo, Yuanzhang Xiao, and Linqi Song. 2022. Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. ACM, Atlanta GA USA, 4289–4293. https://doi.org/10.1145/3511808.3557668
[16]
Zhengyi Ma, Zhicheng Dou, Yutao Zhu, Hanxun Zhong, and Ji-Rong Wen. 2021. One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, Virtual Event Canada, 555–564. https://doi.org/10.1145/3404835.3462828
[17]
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(CIKM ’22). Association for Computing Machinery, New York, NY, USA, 4399–4403. https://doi.org/10.1145/3511808.3557584
[18]
Erasmo Purificato, Ludovico Boratto, and Ernesto William De Luca. 2024. User Modeling and User Profiling: A Comprehensive Survey. https://doi.org/10.48550/arXiv.2402.09660
[19]
Erasmo Purificato and Ernesto William De Luca. 2023. What Are We Missing in Algorithmic Fairness? Discussing Open Challenges for Fairness Analysis in User Profiling with Graph Neural Networks. In Advances in Bias and Fairness in Information Retrieval(Communications in Computer and Information Science), Ludovico Boratto, Stefano Faralli, Mirko Marras, and Giovanni Stilo (Eds.). Springer Nature Switzerland, Cham, 169–175. https://doi.org/10.1007/978-3-031-37249-0_14
[20]
Frederic Raber and Antonio Krüger. 2022. Transferring recommendations through privacy user models across domains. User Modeling and User-Adapted Interaction 32, 1 (April 2022), 25–90. https://doi.org/10.1007/s11257-021-09307-6
[21]
Silvia Schiaffino and Analía Amandi. 2009. Intelligent User Profiling. In Artificial Intelligence An International Perspective: An International Perspective, Max Bramer (Ed.). Springer, Berlin, Heidelberg, 193–216. https://doi.org/10.1007/978-3-642-03226-4_11
[22]
Fajie Yuan, Guoxiao Zhang, Alexandros Karatzoglou, Joemon Jose, Beibei Kong, and Yudong Li. 2021. One Person, One Model, One World: Learning Continual User Representation without Forgetting. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, Virtual Event Canada, 696–705. https://doi.org/10.1145/3404835.3462884
[23]
Mustafa Mert Çelikok, Pierre-Alexandre Murena, and Samuel Kaski. 2023. Modeling needs user modeling. Frontiers in Artificial Intelligence 6 (2023). https://doi.org/10.3389/frai.2023.1097891
Index Terms
- Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends
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Published In
June 2024
662 pages
ISBN:9798400704666
DOI:10.1145/3631700
- General Chairs:
- Ludovico Boratto,
- Cristina Gena,
- Mirko Marras,
- Program Chairs:
- Panagiotis Germanakos,
- Elvira Popescus
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
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Published: 28 June 2024
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UMAP '24: 32nd ACM Conference on User Modeling, Adaptation and Personalization
July 1 - 4, 2024
Cagliari, Italy
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