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The Recommender Systems and Social Computing Lab is a research group in the Department of Computer Science and Biomedical Engineering at the Institute of Interactive Systems and Data Science at Graz University of Technology, Austria. We are committed to producing high-quality, responsible and reproducible research results.

Our research studies recommender systems, user modeling, information retrieval, machine learning, trustworthy AI, and computational social science, with a particular focus on psychological models for recommender systems [1], session-based recommender systems [2], privacy in recommender systems [3], detecting and mitigating bias in recommendation algorithms [4], music recommender system, specifically modeling of non-mainstream music consumption [5], robustness issues of GNNs [6], and NLP-based studies of polarization in social media [7,8].

Selected References:

[1] 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

[2] Lacic, E., Reiter-Haas, M., Kowald, D., Reddy Dareddy, M., Cho, J., & Lex, E. (2020). Using autoencoders for session-based job recommendations. User Modeling and User-Adapted Interaction, 30(4), 617-658.

[3] Muellner, P., Kowald, D., & Lex, E. (2021). Robustness of Meta Matrix Factorization Against Strict Privacy Constraints. In European Conference on Information Retrieval (pp. 107-119). Springer, Cham.

[7] Kowald, D., Schedl, M., & Lex, E. (2020). The unfairness of popularity bias in music recommendation: A reproducibility study. In European Conference on Information Retrieval (pp. 35-42). Springer, Cham

[5] 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), 14.

[6] Hussain, H., Duricic, T., Lex, E., Helic, D., Strohmaier, M., & Kern, R. (2021). Structack: Structure-based Adversarial Attacks on Graph Neural Networks. In Proceedings of the 32nd ACM Conference on Hypertext and Social Media (pp. 111-120).

[7] Reiter-Haas, M., Kopeinik, S., Lex, E. (2021). Studying Moral-based Differences in the Framing of Political Tweets. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 15, pp. 1085-1089).

[8] Reiter-Haas, M., Kloesch, B., Hadler, M., Lex, E. (2022). Polarization of opinions on covid-19 measures: Integrating twitter and survey data. Social Science Computer Review (to appear), 2022.

Recent/Upcoming Tutorials

Current Projects

Selected GitHub Repositories:


Head: Assoc. Prof. Dr. Elisabeth Lex

PhD students:

Master’s students:

Bachelor’s students:




Social media: @socialcomplab