About

Short CV: Univ.-Prof. Dr. Elisabeth Lex is a tenured full professor at Graz University of Technology (TUG). She is dean of study for the master's program Computational Social Systems. She has a habilitation (venia docendi) in Applied Computer Science, and her postdoctoral thesis is on "Modeling and Predicting User Behavior in Web-based Systems". Her areas of expertise include recommender systems, user modeling, behavioral analytics, information retrieval, machine learning, data science, and web mining. Elisabeth Lex received her Ph.D. in Computer Science from Graz University of Technology in 2011. After completing the Ph.D. program, she was a postdoctoral research fellow at Universidad National de San Luis, Argentina, and RWTH Aachen, Germany. Elisabeth was a work package leader in the FP7 IP Learning Layers project, in which she researched cognition-inspired recommender systems and task leader in the H2020 Analytics for Everyday Learning (AFEL) project, in which she researched psychology-informed recommender systems. Elisabeth was a member of the Expert Group on Altmetrics, which advised the European Commission, DG Research and Innovation. The expert group developed policies for the commission on how to use altmetrics to assess the impact of scientific artifacts. She has published more than 150 scientific publications in venues such as WebConf, HT, RecSys, UMAP, ECIR, ISMIR, as well as in journals such as Foundations and Trends in Information Retrieval, UMUAI, EPJ Data Science, Frontiers in AI, Transactions of the International Society for Music Information Retrieval (TISMIR), or the International Journal of Human-Computer Interaction. Elisabeth regularly gives invited talks about her research and acts as Senior PC member, PC member, co-organizer, track chair, and co-track chair at venues such as WebConf, IUI, RecSys, UMAP, Web Science, or HT. Elisabeth is a passionate teacher at TU Graz, where she teaches Web Technology, Recommender Systems, Advanced Information Retrieval and Computational Methods for Statistics.

Research Topics: Recommender Systems, User Modeling, Information Retrieval, Machine Learning, Data Science

Contact: Graz University of Technology (TU Graz)
Sandgasse 36/III
A-8010 Graz, Austria
Email: firstname DOT lastname AT tugraz DOT at

Selected Publications:

• Lex, E., Kowald, D., Seitlinger, P., Tran, T. N. T., Felfernig, A., Schedl, M., (2021). Psychology-informed Recommender Systems. Foundations and Trends in Information Retrieval, 15(2), 134-242 ( .pdf)
• Kowald, D., Muellner, P., Zangerle, E., Bauer, C., Schedl, M. & Lex, E. (2021). Support the Underground: Characteristics of Beyond-Mainstream Music Listeners. EPJ Data Science, 10(1), . ( .pdf)
• Muellner, P., Kowald, D., & Lex, E. (2021). Robustness of Meta Matrix Factorization Against Strict Privacy Constraints. In Proceedings of the 43rd European Conference on Information Retrieval (ECIR'2021). Springer. ( .pdf)
• Lex*, E., Kowald, D., Schedl, M. (2020) Modeling Popularity and Temporal Drift of Music Genre Preferences. Transactions of the International Society for Music Information Retrieval (TISMIR). * both authors contributed equally to this work. ( .pdf)
• Lacic, E., Reiter-Haas, M., Kowald, D., Dareddy, M., Cho, J., & Lex, E. (2020). Using Autoencoders for Session-based Job Recommendations. User Modeling and User-Adapted Interaction (UMUAI). Springer. ( .pdf)

Selected Community Activities:

• Track Co-Chair at ACM UMAP 2022 for Intelligent User Interfaces (with Prof. Marko Tkalcic)
• Senior PC at ACM RecSys
• Senior PC at WebConf
• Track Chair at ACM HT 2020 for Recommender Systems and Social Media
• Senior PC at ACM IUI
• Senior PC at ACM Web Science 2019
• Senior PC at Social Informatics 2019
• Associate Chair at OpenSym 2017
• Best Paper Judging Committee ACM Web Science 2015

Supervised theses

If you are interested in collaboration, and/or in doing a project and/or thesis (bachelor, master) with me, get in touch!