Dr. Dominik Kowald is research area manager of the Social Computing team
at the Know-Center, Austria's leading research center for data-driven business and big data analytics.
Additionally, he is senior researcher at the Institute of Interactive Systems and Data Science of Graz University of Technology.
He has a PhD. (with hons), MSc. (with hons) and BSc. in Computer Science from Graz University of Technology.
He has finished his PhD in October 2017 in the course of the European-funded research project Learning Layers
on cognitive-inspired recommender systems for social tagging and microblogging environments.
Currently he is working as key researcher in the DDAI and DDIA COMET modules, and as task lead in the H2020 TRUSTS project.
He is review editor of Frontiers in Big Data - Recommender Systems section, and his research interests are in the fields of recommender systems, privacy, fairness and biases in algorithms, Web science and computational social science.
Full cv: ()
Teaching certificate: ( .pdf)
Dissertation summary: ( .pdf) ( .pdf)
Selected publications (last 5 years)
- Lacic, E., Fadljevic, L., Weissenboeck, F., Lindstaedt, S., & Kowald, D. (2022). What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations. In Proceedings of the 44th European Conference on Information Retrieval (ECIR'2022). Springer. () .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. () ( .pdf) ( blog) news
- Lex, E., Kowald, D., Seitlinger, P., Tran, T., Felfernig, A., & Schedl, M. (2021). Psychology-informed Recommender Systems. Foundations and Trends in Information Retrieval, Vol. 15, No. 2. () .pdf
- Kowald, D., Schedl, M., & Lex, E. (2020). The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study. In Proceedings of the 42nd European Conference on Information Retrieval (ECIR'2020). Springer. () .pdf
- Kowald, D., Pujari, S., & Lex, E. (2017). Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach. In Proceedings of the 26th International World Wide Web Conference (WWW'2017). ACM. () .pdf