Dr. Dominik Kowald is a post-doctoral researcher in the Social Computing research area
at the Know-Center, Austria's leading research center for data-driven business and big data analytics.
He has a PhD. (with hons), MSc. (with hons) and BSc. in Computer Science from Graz University of Technology,
and before that was attending the College of Industrial Engineering specialising in Manufacturing Computer Science (BULME Graz).
He has finished his PhD in October 2017 in the course of the European-funded research projects Learning Layers and AFEL
on cognitive-inspired recommender systems for social tagging and microblogging environments. Currently he is working as co-task leader in the AI4EU initiative.
Apart from that, he was working in several other industry-driven and European-funded research projects
such as Organic Lingua and MoreGrasp.
His research interests are in the fields of recommender systems, social tagging and microblogging systems, information retrieval, Big Data, Data Science, Open Science and complex network analytics.
Full cv () and dissertation summary ( .pdf).
Selected publications (last five years)
- Hasani-Mavriqi, I., Kowald, D., Helic, D., & Lex, E. (2018). Consensus Dynamics in Online Collaboration Systems. Computational Social Networks Journal. SpringerOpen. () .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
- Trattner, C., Kowald, D., Seitlinger, P., Kopeinik, S., & Ley, T. (2016). Modeling Activation Processes in Human Memory to Predict the Reuse of Tags. The Journal of Web Science (JWS). Vol. 2, No. 1, pp 1 – 18. () .pdf
- Kowald, D., & Lex, E. (2015). Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys'2015). ACM. () ( .pdf) poster
- Kowald, D., Seitlinger, P., Trattner, C., & Ley, T. (2014). Long Time no See: The Probability of Reusing Tags as a Function of Frequency and Recency. In Proceedings of the companion publication of the 23rd international conference on World wide web companion (WWW'2014), pp. 463-468. International World Wide Web Conferences Steering Committee. () .pdf