About me

Dominik Kowald

Priv.-Doz. Dr. Dominik Kowald is research area manager of the FAIR AI group at Know Center Research GmbH, one of Europe's leading research centers for trustworthy AI, and lecturer at the Institute of Human Centred Computing of Graz University of Technology. He has a Habilitation (Priv.-Doz.) in Applied Computer Science, as well as 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 on cognitive-inspired recommender systems based on the cognitive architecture ACT-R. Additionally, in June 2024, he has finished his Habilitation (post-doctoral thesis) on the topic of transparency, privacy, and fairness aspects of recommender systems. Currently, he is working as key researcher in the DDIA COMET module, and in several other applied research projects (see full CV for details). He is review and research topic editor of Frontiers in Big Data - Recommender Systems section, and his research interests are in the fields of trustworthy and reproducible AI, recommender systems, privacy, fairness and biases in algorithms, as well as responsible machine learning and data science. His research on fairness in AI and bias in recommender systems was awarded with the Mind-the-gap gender and diversity award of Graz University of Technology in 2022, and was presented in several news outlets, e.g., by APA science.
Full CV: ( .pdf)
Team lead certificate: ( .pdf)
Lectures: ( databases) ( data management) ( videos)
Teaching certificates: ( advanced) ( basic)
Open theses: ( link)

Selected publications

  • Semmelrock, H., Ross-Hellauer, T., Kopeinik, S., Theiler, D., Haberl, A., Thalmann, S., & Kowald, D. (2025). Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers. AI Magazine, 46(2). ( .pdf)
  • Muellner, P., Lex, E., Schedl, M., & Kowald, D. (2024). The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias. In Proceedings of the 46th European Conference on Information Retrieval (ECIR'2024). Springer. ( .pdf)
  • Scher, S., Kopeinik, S., Truegler, A., & Kowald, D. (2023). Modelling the Long-Term Fairness Dynamics of Data-Driven Targeted Help on Job Seekers. Nature Scientific Reports. ( .pdf)
  • Muellner, P., Lex, E., Schedl, M., & Kowald, D. (2023). ReuseKNN: Neighborhood Reuse for Differentially-Private KNN-Based Recommendations. ACM Transaction on Intelligent Systems and Technology. ( .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)
  • 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)
  • 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)


Publications

Please also have a look at my GoogleScholar, ORCID (0000-0003-3230-6234), ResearchGate, Scopus, and DBLP profiles.

Journal articles (incl. editorials)

  1. Semmelrock, H., Ross-Hellauer, T., Kopeinik, S., Theiler, D., Haberl, A., Thalmann, S., & Kowald, D. (2025). Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers. AI Magazine, 46(2). ( .pdf)
  2. Kowald, D., Scher, S., Pammer-Schindler, V., Müllner, P., Waxnegger, K., Demelius, L., Fessl, A., Toller, M., Mendoza Estrada, I.G., Simic, I., Sabol, V., Truegler, A., Veas, E., Kern, R., Nad, T., & Kopeinik, S. (2024) Establishing and Evaluating Trustworthy AI: Overview and Research Challenges. Frontiers in Big Data, Research Topic on Towards Fair AI for Trustworthy Artificial Intelligence. ( .pdf)
  3. Haberl, A., Fleiß, J., Kowald, D., & Thalmann, S. (2024). Take the aTrain. Introducing an Interface for the Accessible Transcription of Interviews. Journal of Behavioral and Experimental Finance ( .pdf)
  4. Kowald, D., Yang, D., & Lacic, E. (2024). Editorial: Reviews in Recommender Systems. Frontiers in Big Data. ( .pdf)
  5. Scher, S., Kopeinik, S., Truegler, A., & Kowald, D. (2023). Modelling the Long-Term Fairness Dynamics of Data-Driven Targeted Help on Job Seekers. Nature Scientific Reports. ( .pdf)
  6. Muellner, P., Lex, E., Schedl, M., & Kowald, D. (2023). ReuseKNN: Neighborhood Reuse for Differentially-Private KNN-Based Recommendations. ACM Transaction on Intelligent Systems and Technology. ( .pdf)
  7. Muellner, P., Lex, E., Schedl, M., & Kowald, D. (2023). Differential Privacy in Collaborative Filtering Recommender Systems: A Review. Frontiers in Big Data - Reviews in Recommender Systems. ( .pdf)
  8. Duricic, T., Kowald, D., Lacic, E., & Lex, E. (2023). Beyond-accuracy: A review on Diversity, Serendipity, and Fairness in Recommender Systems based on Graph Neural Networks. Frontiers in Big Data - Reviews in Recommender Systems. ( .pdf)
  9. 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)
  10. 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)
  11. Schedl M., Bauer, C., Reisinger, W., Kowald, D., & Lex, E. (2021). Listener Modeling and Context-Aware Music Recommendation Based on Country Archetypes. Frontiers in Artifical Intelligence. ( .pdf)
  12. 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)
  13. Lex, E.*, Kowald, D.*,& Lex, E. (2020). Modeling Popularity and Temporal Drift of Music Genre Preferences. Transactions of the International Society for Music Information Retrieval (TISMIR), 3(1). * both authors contributed equally to this work. ( .pdf)
  14. Ruiz-Calleja, A., Dennerlein, S., Kowald, D., Theiler, D., Lex, E., & Ley, T. (2019). An Infrastructure for Workplace Learning Analytics: Tracing Knowledge Creation with the Social Semantic Server. Journal of Learning Analytics. SoLAR. ( .pdf)
  15. Hasani-Mavriqi, I., Kowald, D., Helic, D., & Lex, E. (2018). Consensus Dynamics in Online Collaboration Systems. Computational Social Networks Journal. SpringerOpen. ( .pdf)
  16. Seitlinger, P., Ley, T., Kowald, D., Theiler, D., Hasani-Mavriqi, I., Dennerlein, S., Lex, E., & Albert, D. (2017). Balancing the Fluency-Consistency Tradeoff in Collaborative Information Search with a Recommender Approach. International Journal of Human–Computer Interaction (HCI). ( .pdf)
  17. Kopeinik, S., Kowald, D., Hasani-Mavriqi, I., & Lex, E. (2017). Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning. The Journal of Web Science (JWS). Vol. 2, No 4, pp 45 - 61 ( .pdf)
  18. 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)
  19. Santos, P., Dennerlein, S., Theiler, D., Cook, J., Treasure-Jones, T., Holley, D., Kerr, M., Attwell, G., Kowald, D., & Lex, E. (2016) Going beyond your personal learning network, using recommendations and trust through a multimedia question-answering service for decision-support: A case study in the healthcare. Journal of Universal Computer Science (JUCS). ISSN 0948-695X. ( .pdf)

Books and book chapters

  1. Kowald, D., Reiter-Haas, M., Kopeinik, S., Schedl, M., & Lex, E. (2024). Transparent Music Preference Modeling and Recommendation with a Model of Human Memory Theory. A Human-centered Perspective of Intelligent Personalized Environments and Systems. ( .pdf)
  2. Kowald, D. (2017). Modeling Activation Processes in Human Memory for Tag Recommendations: Using Models from Human Memory Theory to Implement Recommender Systems for Social Tagging and Microblogging Environments. Suedwestdeutscher Verlag fuer Hochschulschriften. ISBN: 978-620-2-32072-6. ( .pdf)
  3. Kowald, D., Kopeinik, S., Seitlinger, P., Ley, T., Albert, D., & Trattner, C. (2015). Refining Frequency-Based Tag Reuse Predictions by Means of Time and Semantic Context. Mining, Modeling, and Recommending'Things' in Social Media (pp. 55-74). Springer International Publishing. ( .pdf)
  4. Kowald, D., Seitlinger, P., Kopeinik, S., Ley, T., & Trattner, C. (2015). Forgetting the Words but Remembering the Meaning: Modeling Forgetting in a Verbal and Semantic Tag Recommender. Mining, Modeling, and Recommending'Things' in Social Media (pp. 75-95). Springer International Publishing. ( .pdf)
  5. Lacic, E., Kowald, D., Eberhard, L., Trattner, C., Parra, D., & Marinho, L. B. (2015). Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces. Mining, Modeling, and Recommending'Things' in Social Media (pp. 96-115). Springer International Publishing. ( .pdf)

Peer-reviewed conference contributions

  1. Kowald, D. (2025). Investigating Popularity Bias Amplification in Recommender Systems Employed in the Entertainment Domain. In Proceedings of the 4th European Workshop on Algorithmic Fairness (EWAF'2025) ( .pdf)
  2. Autischer, G., Waxnegger, K., & Kowald, D.. (2025). AI Certification and Assessment Catalogues: Practical Use and Challenges in the Context of the European AI Act. In Proceedings of the 4th European Workshop on Algorithmic Fairness (EWAF'2025). ( .pdf)
  3. Innerebner, K., Kowald, D., Schedl, M., & Lex, E. (2025). Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R. In Adjunct Proceedings of UMAP'2025 ( .pdf)
  4. Lex, E., Innerebner, K., Tkalcic, M., Kowald, D. & Schedl, M. (2025). Hybrid AI for Human-Centric Personalization (HyPer). In Adjunct Proceedings of UMAP'2025. ( .pdf)
  5. Lesota, O., Geiger, J., Walder, M., Kowald, D., & Schedl, M. (2024). Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems. In Proceedings of the 18th ACM Conference on Recommender Systems (RecSys'2024). ( .pdf)
  6. Atzenhofer-Baumgartner, F., Geiger, B., Vogeler, G., & Kowald, D. (2024). Value Identification in Multistakeholder Recommender Systems for Humanities and Historical Research: The Case of the Digital Archive Monasterium net. NORMalize workshop co-located with RecSys'24. ( .pdf)
  7. Atzenhofer-Baumgartner, F., Geiger, B., Trattner, C., Vogeler, G., & Kowald, D. (2024). Challenges in Implementing a Recommender System for Historical Research in the Humanities. 1st edition of the AltRecSys workshop co-located with RecSys'24. ( .pdf)
  8. Duricic, T., Muellner, P., Weidinger, N., Elsayed, N., Kowald, D., & Veas, E. (2024). AI-Powered Immersive Assistance for Interactive Task Execution in Industrial Environments. In Proceedings of the 50th European Conference on Artificial Intelligence (ECAI'2024). ( .pdf)
  9. Escobeda, G., Moscati, M., Muellner, P., Kopeinik, S., Kowald, D., Lex, E., & Schedl, M. (2024). Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models. In Proceedings of ECML-PKDD'2024. Springer. ( .pdf)
  10. Muellner, P., Lex, E., Schedl, M., & Kowald, D. (2024). The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias. In Proceedings of the 46th European Conference on Information Retrieval (ECIR'2024). Springer. ( .pdf)
  11. Koenigstorfer, F., Haberl, A., Kowald, D., Ross-Hellauer, T., & Thalmann, S. (2024). Black Box or Open Science? Assessing Reproducibility-Related Documentation in AI Research. In Proceedings of the 57. Hawaii International Conference on System Sciences (HICSS'2024). ( .pdf)
  12. Moscati, M., Wallmann, C., Reiter-Haas, M., Kowald, D., Lex, E., & Schedl, M. (2023). Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems (RecSys'2023), ACM. ( .pdf)
  13. Kowald, D.*, Mayr, G.*, Schedl, M., & Lex, E. (2023). A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations. In Advances in Bias and Fairness in Information Retrieval. BIAS 2023. Communications in Computer and Information Science. Springer. * both authors contributed equally to this work.( .pdf)
  14. Lacic, E., Duricic, T., Fadljevic, L., Theiler, D., & Kowald, D. (2023). Uptrendz: API-Centric Real-Time Recommendations in Multi-Domain Settings. In Proceedings of the 45th European Conference on Information Retrieval (ECIR'2023). Springer. ( .pdf)
  15. 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)
  16. Kowald, D., & Lacic, E. (2022). Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems. In Advances in Bias and Fairness in Information Retrieval. BIAS 2022. Communications in Computer and Information Science, vol 1610. Springer. ( .pdf) ( award)
  17. Muellner, P., Schmerda, S., Theiler, D., Lindstaedt, S., & Kowald, D. (2022). Towards Employing Recommender Systems for Supporting Data and Algorithm Sharing. In Proceedings of the DataEconomy Workshop co-located with the 18th International Conference on emerging Networking EXperiments and Technologies (CoNext'2022). ACM. ( .pdf)
  18. Lacic, E., & Kowald, D. (2022). Recommendations in a Multi-Domain Setting: Adapting for Customization, Scalability and Real-Time Performance. In Industry-Day Track of European Conference on Information Retrieval (ECIR'2022). ( .pdf)
  19. Lesota, O., Melchiorre, A., Rekabsaz, N., Brandl, S., Kowald, D., Lex, E., & Schedl, M. (2021). Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?. In Proceedings of the 15th ACM Conference on Recommender Systems (RecSys'2021), Late-Breaking Results, ACM.( .pdf)
  20. Duricic, T., Kowald, D., Schedl, M., & Lex, E. (2021). My friends also prefer diverse music: homophily and link prediction with user preferences for mainstream, novelty, and diversity in music. In Proceedings of International Conference on Advances in Social Network Analysis and Mining/MSNDS Workshop (ASONAM'2021). IEEE/ACM. ( .pdf)
  21. Muellner, P., Lex, E., & Kowald, D. (2021). Position Paper on Simulating Privacy Dynamics in Recommender Systems. In Simulation for Recommender Systems Workshop (SimuRec'2021) co-located with ACM Conference on Recommender Systems (RecSys'2021). ( .pdf)
  22. 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)
  23. 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)
  24. Kowald, D.*, Lex, E.*, & Schedl, M. (2020). Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations. In Proceedings of the Humanize workshop co-located with the 25th ACM Conference on Intelligent User Interfaces (IUI'2020). ACM. * both authors contributed equally to this work. ( .pdf)
  25. Duricic, T., Hussain, H., Lacic, E., Kowald, D., Helic, D., & Lex, E. (2020). Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering. In Proceedings of the 25th International Symposium on Intelligent Systems (ISMIS'2020). Springer. ( .pdf)
  26. Fadljevic, S.*, Maitz, K.*, Kowald, D., Pammer-Schindler, V., & Gasteiger-Klipcera, B. (2020). Slow is Good: The Effect of Diligence on Student Performance in the Case of an Adaptive Learning System for Health Literacy. In Proceedings of the 10th International Learning Analytics and Knowledge Conference (LAK'2020). ACM. * both authors contributed equally to this work. ( .pdf)
  27. Kopeinik, S., Lex, E., Kowald, D., Albert, D., & Seitlinger, P. (2019). A Real-Life School Study of Confirmation Bias and Polarisation in Information Behaviour. In Proceedings of the 14th European Conference on Technology Enhanced Learning (ECTEL'2019). Springer. ( .pdf)
  28. Kowald, D*., Lex, E.*, & Schedl, M. (2019). Modeling Artist Preferences for Personalized Music Recommendations. In Late-Breaking-Results of the 20th annual conference of the International Society for Music Information Retrieval (ISMIR'2019). * both authors contributed equally to this work. ( .pdf)
  29. Lacic, E.*, Kowald, D.*, Theiler, D., Traub, M., Kuffer, L., Lindstaedt, S., & Lex, E. (2019). Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metric. In REVEAL Workshop co-located with ACM Conference on Recommender Systems (RecSys'2019). * both authors contributed equally to this work. ( .pdf) ( poster)
  30. Kowald, D., Traub, M., Theiler, D., Gursch, H., Lindstaedt, S., Kern, R., & Lex, E. (2019). Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets. In REVEAL Workshop co-located with ACM Conference on Recommender Systems (RecSys'2019). ( .pdf) ( poster)
  31. Lex, E., & Kowald, D. (2019). The Impact of Time on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach. In Proceedings of The 49th GI Annual Conference (INFORMATIK'2019). ( .pdf)
  32. Kowald, D.*, Lex, E.*, & Schedl, M. (2019). Modeling Artist Preferences of Users with Different Music Consumption Patterns for Fair Music Recommendations. European Symposium on Computational Social Science (EUROCSS'2019). * both authors contributed equally to this work. ( .pdf) ( poster)
  33. Duricic, T., Lacic, E., & Kowald, D. & Lex, E. (2019). Exploiting weak ties in trust-based recommender systems using regular equivalence. European Symposium on Computational Social Science (EUROCSS'2019). ( .pdf) ( poster)
  34. Duricic, T., Lacic, E., Kowald, D., & Lex, E. (2018). Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys'2018). ACM. ( .pdf) ( poster)
  35. Kowald, D., Seitlinger, P., Ley, T., & Lex, E. (2018). The Impact of Semantic Context Cues on the User Acceptance of Tag Recommendations: An Online Study. In Companion Proceedings of the 27th International World Wide Web Conference (WWW'2018). ACM. ( .pdf) ( poster)
  36. D'Aquin, M., Kowald, D., Fessl, A., Lex, E., & Thalmann, S. (2018). AFEL - Analytics for Everyday Learning. In International Projects Track co-located with the 27th International World Wide Web Conference (WWW'2018). ACM. ( .pdf)
  37. Kowald, D., Lacic, E., Theiler, D., & Lex, E. (2018). AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments. In Social Interaction-Based Recommender Systems (SIR'2018) Workshop co-located with Conference on Information and Knowledge Management (CIKM'2018) conference. ( .pdf)
  38. Lacic, E., Kowald, D., & Lex, E. (2018). Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations. In International Workshop on Entity Retrieval (EYRE'2018) Workshop co-located with Conference on Information and Knowledge Management (CIKM'2018) conference. ( .pdf)
  39. Fessl, A., Kowald, D., Lopez-Sola, S., Moreno, A., Maturano, R., & Thalmann, S. (2018). Analytics for Everyday Learning from Two Perspectives: Knowledge Workers and Teachers. In Analytics for Everyday Learning (AFEL'2018) Workshop co-located with European Conference on Technology Enhanced Learning (ECTEL'2018) conference. ( .pdf)
  40. Dennerlein, S., Kowald, D., Pammer-Schindler, V., Lex, E., & Ley, T. (2018). Simulation-based Co-Creation of Algorithms. In Workshop on Co-Creation in the Design, Development and Implementation of Technology-Enhanced Learning (CCTEL'2018) co-located with European Conference on Technology Enhanced Learning (ECTEL'2018) conference. ( .pdf)
  41. Lacic, E., Kowald, D., Reiter-Haas, M., Slawicek, V., & Lex, E. (2018). Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendations. In International Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization (IFUP'2018) co-located with the 11th ACM International Conference on Web Search and Data Mining (WSDM'2018). ( .pdf)
  42. Kowald, D., & Lex, E. (2018). Studying Confirmation Bias in Hashtag Usage on Twitter. European Symposium on Computational Social Science (EUROCSS'2018). ( .pdf) ( poster)
  43. Lex, E., Wagner, M., & Kowald, D. (2018). Mitigating Confirmation Bias on Twitter by Recommending Opposing Views. European Symposium on Computational Social Science (EUROCSS'2018). ( .pdf) ( poster)
  44. Kowald, D., Kopeinik, S., & Lex, E. (2017). The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems. In Adjunct Publication of the 25th Conference on User Modeling, Adapation and Personalization (UMAP'2017). ACM. ( .pdf) ( poster)
  45. 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)
  46. D'Aquin, M., Adamou, A., Dietze, S., Fetahu, B., Gadiraju, U., Hasani-Mavriqi, I., Holtz, P., Kimmerle, J., Kowald, D., Lex, E., Lopez.Sola, S., Maturana, R., Sabol, V., Troullinou, P., & Veas, E. (2017). AFEL: Towards Measuring Online Activities Contributions to Self-directed Learning. In Proceedings of Proceedings of the 7th Workshop on Awareness and Reflection in Technology Enhanced Learning (ARTEL) in conjunction with the 12th European Conference on Technology Enhanced Learning: Adaptive and Adaptable Learning (EC-TEL 2017) ( .pdf)
  47. Lacic, E., Kowald, D., & Lex, E. (2017). Tailoring Recommendations for a Multi-Domain Environment. Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning (RecSysKTL'2017) co-location with the 11th ACM Conference on Recommender Systems (RecSys'2017). ( .pdf)
  48. Kowald, D., & Lex, E. (2017). Overcoming the Imbalance Between Tag Recommendation Approaches and Real-World Folksonomy Structures with Cognitive-Inspired Algorithms. European Symposium on Computational Social Science (EUROCSS'2017). ( .pdf) ( poster)
  49. Kopeinik, S., Kowald, D., & Lex, E. (2016). Which Algorithms Suit Which Learning Environments? A Comparative Study of Recommender Systems in TEL. In Proceedings of the 11th European Conference on Technology Enhanced Learning (EC-TEL'2016). Springer. ( .pdf)
  50. Kowald, D., & Lex, E. (2016). The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems. In Proceedings of the 27th International Conference on Hypertext and Social Media (HT'2016). ACM. ( .pdf)
  51. Lacic, E., Kowald, D., & Lex, E. (2016). High Enough? Explaining and Predicting Traveler Satisfaction in Airline Reviews. In Proceedings of the 27th International Conference on Hypertext and Social Media (HT'2016). ACM. ( .pdf)
  52. Traub, M., Lacic, E., Kowald, D., Kahr, M., & Lex, E. (2016). Need Help? Recommending Social Institutions. Workshop on Recommender Systems and Big Data Analytics (RSBDA'2016) co-location with i-KNOW'2016. ( .pdf)
  53. 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)
  54. Seitlinger, P., Kowald, D., Kopeinik, S., Hasani-Mavriqi, I., Ley, T., & Lex, E. (2015). Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics. In Proceedings of the companion publication of the 24th international conference on World wide web companion (WWW'2015). International World Wide Web Conferences Steering Committee. ( .pdf)
  55. Kowald, D. (2015). Modeling Cognitive Processes in Social Tagging to Improve Tag Recommendations. In Proceedings of the 24th International Conference on World Wide Web Companion (WWW'2015), pp. 505-509. International World Wide Web Conferences Steering Committee. (PhD Symposium) ( .pdf)
  56. Lacic, E., Kowald, D., Traub, M., Luzhnica, G., Simon, J., & Lex, E. (2015). Tackling Cold-Start Users in Recommender Systems with Indoor Positioning Systems. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys'2015). CEUR-WS. ( .pdf) ( poster)
  57. Traub, M., Kowald, D., Lacic, E., Schoen, P., Supp, G., & Lex, E. (2015). Smart booking without looking: providing hotel recommendations in the TripRebel portal. In Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business (i-Know'2015). ACM. (best demo honourable mention). ( .pdf) ( poster)
  58. Dennerlein, S., Kowald, D., Lex, E., Theiler, D., Lacic, E., & Ley, T. (2015). The Social Semantic Server: A Flexible Framework to Support Informal Learning at the Workplace. In Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business(i-Know'2015). ACM. ( .pdf)
  59. Lacic, E., Traub, M., Kowald, D., & Lex, E. (2015). ScaR: Towards a Real-Time Recommender Framework Following the Microservices Architecture. Workshop on Large Scale Recommender Systems (LSRS'2015) co-located with the 9th ACM Conference on Recommender Systems (RecSys'2015). ( .pdf)
  60. Kowald, D., Seitlinger, P., Ley, T., & Lex, E. (2015). Modeling Activation Processes in Human Memory to Improve Tag Recommendations. 2nd GESIS Computational Social Sciences Winter Symposium (CSSWS’2015). ( .pdf) ( poster)
  61. 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)
  62. Kowald, D., Lacic, E., & Trattner, C. (2014). TagRec: towards a standardized tag recommender benchmarking framework. In Proceedings of the 25th ACM conference on Hypertext and social media (HT'2014) ,pp. 305-307). ACM. (best poster award). ( .pdf) ( poster)
  63. Lacic, E., Kowald, D., & Trattner, C. (2014). SocRecM: a scalable social recommender engine for online marketplaces. In Proceedings of the 25th ACM conference on Hypertext and social media (HT'2014), pp. 308-310. ACM. ( .pdf) ( poster)
  64. Lacic, E.*, Kowald, D.*, Seitlinger, P., Trattner, C. & Parra, D. (2014). Recommending Items in Social Tagging Systems Using Tag and Time Information, In 1st International Workshop on Social Personalisation (SP'2014) co-located with the 25th ACM Conference on Hypertext and Social Media (HT'2014). CEUR-WS. * both authors contributed equally to this work. ( .pdf)
  65. Lacic, E., Kowald, D., Parra, D., Kahr, M., & Trattner, C. (2014). Towards a scalable social recommender engine for online marketplaces: The case of apache solr. In Proceedings of the companion publication of the 23rd international conference on World wide web companion (WWW'2014), pp. 817-822. Workshop on Social Recommender Systems: SRS'2014. ( .pdf)
  66. Seitlinger, P., Kowald, D., Trattner, C., & Ley, T. (2013). Recommending tags with a model of human categorization. In Proceedings of the 22nd ACM international conference on information & knowledge management (CIKM'2013), pp. 2381-2386. ACM. ( .pdf)
  67. Kowald, D., Dennerlein, S., Theiler, D., Walk, S., & Trattner, C. (2013). The Social Semantic Server - A Framework to Provide Services on Social Semantic Network Data. In Proceedings of the 9th International Conference on Semantic Systems (i-Semantics'2013). CEUR-WS. ( .pdf) ( poster)

Proceedings as editors

  1. Kowald, D., Yang, D., & Lacic, E. (2024). Reviews in Recommender Systems: 2022. In Frontiers in Big Data. ( .pdf)
  2. Alexander Felfernig, Ralf Klamma, Tobias Ley, Dominik Kowald, Elisabeth Lex, & Viktoria Pammer-Schindler (2017). Focused topic on "Recommender systems and social network analysis" in Journal of Universal Computer Science (JUCS). Volume 23. Issue 9. ( link)
  3. Mario Aehnelt, Olivia Bluder, Gert Breitfuss, Rene Kaiser, Roman Kern, Ralf Klamma, Dominik Kowald, Tobias Ley, Elisabeth Lex, Christiana Müller, Viktoria Pammer-Schindler, Romana Rauter, Gerald Reiner, & Eduardo Veas (2017). Proceedings of the Workshop Papers of i-Know 2017, Graz, Austria, October 11-12, 2017. ( link)

Extended abstracts (peer-reviewed) and pre-prints

  1. Burke, R., Adomavicius, G., Bogers, T., Di Noia, T., Kowald, D., Neidhardt, J., Özgöbek, Ö., Pera, S., Tintarev, N., & Ziegler, J. (2025). De-centering the (Traditional) User: Multistakeholder Evaluation of Recommender Systems. Preprint (under review in International Journal on Human Computer Studies). ( .pdf)
  2. Forster, V., Fleiß, J., Kowald, D., & Robertson, V. (2025). Detecting Resale Price Maintenance with Unsupervised Machine Learning. Preprint (under review in Journal of Competition Law and Economics) ( .pdf)
  3. Autischer, G., Waxnegger, K., & Kowald, D.. (2025). Practical Application and Limitations of AI Certification Catalogues in the Light of the AI Act. Preprint. ( .pdf)
  4. Semmelrock, H., Kopeinik, S., Theiler, D., Ross-Hellauer, T., & Kowald, D. (2023). Reproducibility in Machine Learning-Driven Research. Preprint. ( .pdf)
  5. Scher, S., Geiger, B., Kopeinik, S., Trügler, A., & Kowald, D. (2023). A Conceptual Model for Leaving the Data-Centric Approach in Machine Learning. Preprint. ( .pdf)
  6. Kowald, D. (2017). Modeling Activation Processes in Human Memory to Improve Tag Recommendations. SIGIR Forum December 2017, Volume 51, Number 3 (dissertation abstract). ACM. ( .pdf)
  7. Trattner, C., Kowald, D., & Lacic, E. (2015). TagRec: Towards a Toolkit for Reproducible Evaluation and Development of Tag-Based Recommender Algorithms. SIGWEB Newsletter. Winter 2015. ( .pdf)

Other publications and theses

  1. Burke, R., Adomavicius, G., Bogers, T., Di Noia, T., Kowald, D., Neidhardt, J., Özgöbek, Ö., Pera, S., & Ziegler, J. (2024). Multistakeholder and Multimethod Evaluation. Dagstuhl Seminar on Evaluation Perspectives of Recommender Systems. ( .pdf)
  2. Kowald, D. (2024). Transparency, Privacy, and Fairness in Recommender Systems. Habilitation (post-doctoral thesis), Graz University of Technology. ( .pdf)
  3. Scher, S., Kopeinik, S., Wolf-Breener, C., Waxnegger, K., Nad, T., & Kowald, D. (2024). Trustworthy AI Evaluation Methods and Frameworks. SGS white paper series on Trustworthy AI. ( pdf)
  4. Muellner, P., Lex, E., & Kowald, D. (2021). Impact of Meta Learning for Privacy-Preserving Recommender Systems. In The Responsible AI Forum (TRAIF'2021). ( .pdf)
  5. Traub, M., Gursch, H., Kowald, D., Theiler, D., Kern, R., & Lex, E. (2018). Providing Recommendations of Services, Datasets and End-Users in the Data Market Austria (DMA). In International Workshop on Decision Making and Recommender Systems (DMRS'2018). ( .pdf)
  6. Lex, E., Ross-Hellauer, T., & Kowald, D. (2018). Recommender Systems as Enabling Technology to Interlink Scholarly Information. In Workshop on Researcher Centric Scholarly Communication co-located with the 27th International World Wide Web Conference (WWW'2018). ( .pdf) ( link)
  7. Kowald, D. (2017). Modeling Activation Processes in Human Memory to Improve Tag Recommendations. PhD thesis, Graz University of Technology. ( .pdf) ( summary) ( slides)
  8. Kowald, D. (2012). Combining Computer-Supported, Collaborative Learning with E-Assessment: Enhancing a Wiki System with Flexible Assessment Methods. Master thesis, Graz University of Technology. ( .pdf)
  9. Kowald, D., & Maderer, J. (2009). Peer Assessment in Computer Science and Modern Technologies to Build a Flexible E-Learning System around it. Bachelor thesis, Graz University of Technology. ( .pdf)


Services

Scientific workshop and hackathon organization, and session chairing

  • HyPer'2025, Co-Organizer of the Hybrid AI for Human-Centric Personalization Workshop co-located with UMAP'25, New York City, United States, 2025 ( link)
  • ReproAI'2025, Co-Organizer of the Reproducibility and AI Workshop co-located with "Yes, we are open" Weizenbaum Institute Conference, Berlin, Germany, 2025 ( link)
  • STS'2025, Co-Organizer of the Fairness and Artificial Intelligence workshop at the STS conference, Graz, Austria, 2025 ( link)
  • DARIAH'2025, Co-Organizer of the Efficient Similarity Hackathon for Advanced Search in Cultural Heritage, Graz, Austria, 2025 ( link)
  • CRBAM'2024, Co-Organizer of the Fair Recommendations for Cyclists workshop at 8th Annual Meeting of the Cycling Research Board, Zurich, Switzerland, 2024 ( link)
  • DIH-Sued'2022, Co-Organizer of DIH-Sued workshop on recommender systems, Graz, Austria, 2022 ( link)
  • SummerAcademy'2020, Co-Organizer of Know-Center summer academy on recommender systems, Graz, Austria, 2020 ( link)
  • CIKM'2018, Session chair of the Recommendation track of the ACM Conference on Information and Knowledge Management, Turin, Italy, 2018 ( link)
  • RSBDA'2017, Co-Organizer of the Second Workshop on Recommender Systems and Big Data Analytics co-located with i-KNOW 2017, Graz, Austria, 2017. ( link)
  • RSBDA'2016, Co-Organizer of the First Workshop on Recommender Systems and Big Data Analytics co-located with i-KNOW 2016, Graz, Austria, 2016. ( link)
  • i-Know'2015, Session chair of the Social Computing track of the 15th International Conference on Knowledge Technologies and Data-Driven Business, Graz, Austria, 2015 ( link)
  • i-Know'2013, Session chair of the Science 2.0 track of the 13th International Conference on Knowledge Technologies and Data-Driven Business, Graz, Austria, 2013 ( link)

Programm committee membership and reviewing

  • RecSys (senior PC), Reproducibility track of the ACM Conference on Recommender Systems, 2025 ( link)
  • ECAI (senior PC), 28th European Conference on Artifical Intelligence, since 2025 ( link)
  • TORS, ACM Transactions on Recommender Systems, since 2024 ( link)
  • SIGIR, 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, since 2024 ( link)
  • ECAI, 27th European Conference on Artifical Intelligence, since 2024 ( link)
  • IronGraphs, First International Workshop on Graph-Based Approaches in Information Retrieval co-located with ECIR, 2024 ( link)
  • MURS, 2nd Workshop on Music Recommender Systems co-located with RecSys, 2024 ( link)
  • PsyIAS, First Workshop on Psychology-informed Information Access Systems co-located with WSDM, 2024 ( link)
  • Humanize Book, A Human-centered Perspective of Intelligent Personalized Environments and Systems (Springer), 2024 ( link)
  • ECIR, European Conference on Information Retrieval, since 2024 ( link)
  • ECIR (senior PC), Reproducibility track of the European Conference on Information Retrieval, 2023 ( link)
  • INTRS, Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with RecSys, 2023 ( link)
  • ICHCI, International Journal of Human–Computer Interaction, 2022 ( link)
  • HAAPIE, International Workshop on Human Aspects in Adaptive and Personalized Interactive Environments (HAAPIE) co-located with UMAP, 2022 ( link)
  • ICWE, International Conference on Web Engineering, 2022 ( link)
  • FRONTIERS (review editor), Frontiers in Big Data - Section Recommender Systems, since 2021 ( link)
  • TIST, ACM Transactions on Intelligent Systems and Technology, 2021 ( link)
  • MORS, Workshop on Multi-Objective Recommender Systems co-located with RecSys, since 2021 ( link)
  • PERSPECTIVES, Perspectives on the Evaluation of Recommender Systems co-located with RecSys, since 2021 ( link)
  • ASC, Applied Soft Computing, 2021 ( link)
  • CIKM, ACM International Conference on Information and Knowledge Management, since 2020 ( link)
  • FRONTIERS, Frontiers in Psychology, 2020 ( link)
  • TheWebConf, International World Wide Web Conference, since 2020 ( link)
  • IUI, ACM Conference on Intelligent User Interfaces, since 2020 ( link)
  • RDSM, International Workshop on Rumours and Deception in Social Media co-located with COLING conference, 2020 ( link)
  • EPJ, EPJ Data Science, 2019 ( link)
  • TWEB, ACM Transactions on the Web, 2019 ( link)
  • HT, ACM Conference on Hypertext and Social Media, since 2019 ( link)
  • EUROCSS, European Symposium on Computational Social Science, 2019 ( link)
  • TCSS, IEEE Transactions on Computational Social Systems, 2019 ( link)
  • ASC, Applied Soft Computing, since 2019 ( link)
  • PlosOne, PlosOne Journal, 2018 ( link)
  • Systems and Software, Elsevier Journal of Systems and Software, 2018 ( link)
  • TKDE, IEEE Transactions of Knowledge and Data Management, 2018 ( link)
  • RecSys, ACM Conference on Recommender Systems, since 2018 ( link)
  • SoAPS, Workshop on Social Aspects in Personalization and Search co-located with ECIR conference, 2018 ( link)
  • AFEL, Analytics for Everyday Learning Workshop co-located with ECTEL conference, 2018 ( link)
  • INRT Journal, Information Retrieval Journal, 2018 ( link)
  • AJSE, Arabian Journal for Science and Engineering, 2018 ( link)
  • TLT Journal, Transactions on Learning Technologies, 2017 ( link)
  • SNAMS, The Fourth International Symposium on Social Networks Analysis, Management and Security, 2017 ( link)
  • WebSci, 9th International ACM Web Science Conference, since 2017 ( link)
  • OpenSym, 13th International Symposium on Open Collaboration, Galway, Ireland, 2017 ( link)
  • SNAM Journal, Social Network Analysis and Mining, 2017 ( link)
  • Computers & Education Journal, 2017 ( link)
  • MSM, International Workshop on Modeling Social Media - Behavioral Analytics in Social Media, Big Data and the Web co-located with WWW conference, since 2015 ( link)
  • UMAP, ACN Conference on User Modelling, Adaption and Personalization, since 2014 ( link)
  • EC-TEL, European Conference on Technology Enhanced Learning, since 2014 (since 2020 as leading reviewer) ( link)

Presentations at international conferences and events

  • HabilTalk'2025, Habilitation presentation at the Faculty for Computer Science and Biomedical Engineering of TU Graz, Graz, Austria. ( slides)
  • RecSys'2024, Short paper track of the 18th ACM Conference on Recommender Systems, Bari, Italy. ( poster)
  • AIKnow'2024, Talk on evaluation and certification of trustworthy AI at AI-Know'2025, Graz, Austria. ( slides)
  • OEGGF'2024, Talk on fair AI in the labor market at the 10. Tagung der Österreichischen Gesellschaft für Geschlechterforschung (OEGGF) Graz, Austria. ( slides)
  • MF'2024, Invited talk on Transparency, Privacy, and Fairness in Recommender Systems at MediaFutures Bergen, Norway. ( link)
  • KFU'2024, Invited talk on Trustworthy AI and its Connection to Reproducibility at Karl-Franzens University, Graz, Austria. ( slides)
  • WF'2024, Invited speaker and panelist on fair AI in the labor market at the Wissenschaftsforum, Köln, Germany. ( slides)
  • ECIR'2023, Demo/poster session of the 45th European Conference on Information Retrieval, Dublin, Ireland. ( poster)
  • BIAS'2023, Bias Workshop co-located with the 45th European Conference on Information Retrieval, Dublin, Ireland. ( slides)
  • EBDVA'2022, European Big Data Value Forum 2022, panel discussion on trustworhty AI and EU AI Act, Prague, Czech Republic. ( link)
  • TUG'2022, TU Graz, Science for future day, poster presentation on fair AI, Graz, Austria. ( link)
  • FHJ'2022, FH Joanneum, Journalism course, Summer-term 2022, invited external lecture on recommender systems in media and beyond, Graz, Austria. ( link)
  • ECIR'2022, Industry track of the 44th European Conference on Information Retrieval, Stavanger, Norway. ( poster) ( slides)
  • ECIR'2022, Poster session of the 44th European Conference on Information Retrieval, Stavanger, Norway. ( poster)
  • BIAS'2022, Bias Workshop co-located with the 44th European Conference on Information Retrieval, Stavanger, Norway. ( slides)
  • PhdRetreat'2021, Presentation in Social Data Science session as part of Know-Center and ISDS@TU Graz Phd retreat, Loipersdorf, Austria. ( slides)
  • DataWeek'2021, Panel and presentation on Breaking silos in data innovation in Europe as part of BDVA Data Week. (online due to COVID-19). ( slides)
  • ECIR'2020, Reproducibility session of the 42nd European Conference on Information Retrieval, Lisbon, Portugal (online due to COVID-19). ( slides)
  • RecSys'2019, Poster session of the REVEAL Workshop co-located with RecSys'2019 conference, Copenhagen, Denmark. ( poster) ( poster)
  • EUROCSS'2019, Pecha Kucha and poster sessions of the Third European Symposium on Computational Social Science in Zurich, Switzerland (with travel grant). ( slides) ( poster)
  • CSS-SummerSchoool'2019, Pecha Kucha and mini project sessions of the Third Summer School on Computational Social Science in Berlin, Germany. ( slides)
  • EUROCSS'2018, Pecha Kucha and poster sessions of the Second European Symposium on Computational Social Science in Cologne, Germany (with travel grant). ( slides) ( poster)
  • CIKM'2018, Paper session of the Social Interaction-Based Recommender Systems Workshop co-located with CIKM'2018 in Turin, Italy. ( slides)
  • WWW'2018, Poster session of the 27th International World Wide Web Conference in Lyon, France. ( poster)
  • EUROCSS'2017, Algorithms paper and poster sessions of the First European Symposium on Computational Social Science in London, Great Britain. ( slides) ( poster)
  • UMAP'2017, Poster session of the 25th Conference on User Modeling, Adaption and Personalization in Bratislava, Slovakia. ( poster)
  • WWW'2017, Data Mining paper session of the 26th International World Wide Web Conference in Perth, Australia. ( slides)
  • HT'2016, Social Media Analytics paper session of the 27th ACM Conference on Hypertext and Social Media in Halifax, Canada (with travel grant). ( slides)
  • CSSWS'2015, Pecha Kucha and poster session of the 2nd Computational Social Sciences Winter Symposium, Cologne, Germany. ( poster)
  • RecSys’2015, Short Paper slam and poster session of the 9th ACM Conference on Recommender Systems, Vienna, Austria. ( poster) ( poster)
  • WWW'2015, PhD Symposium of the 24th International World Wide Web Conference, Florence, Italy. ( slides)
  • i-Know'2015, Demo session of 15th Int. Conference on Knowledge Technologies, Graz, Austria. ( poster)
  • WWW'2014, WebScience track paper session of the 23rd International World Wide Web Conference, Seoul, Korea. ( slides)
  • WWW'2014, Paper session of the workshop on Social Recommender Systems co-located with WWW’2014 conference, Seoul, Korea. ( slides)
  • i-Semantics’2013, Poster session of the 9th Int. Conference on Semantic Systems, Graz, Austria. ( poster)

Awards

  • Outstanding Reviewer Award at the 32nd ACM Conference on User Modeling, Adaption and Personalization (UMAP'2024). 2024 ( link)
  • Mind-the-Gap Gender and Diversity Award by TU Graz, Austria. 2022 ( link)
  • Dissertation Award by Arbeiterkammer Steiermark, Austria. 2018 ( link)
  • Nominated for ACM SIGCHI Outstanding Dissertation award by Institute of Interactive Systems and Data Science of Graz University of Technology. 2018 ( link)
  • Nominated for Award of Excellence by Faculty of Informatics of Graz University of Technology for dissertation. 2018 ( link)
  • Nominated for Heinz Zemanek Award by Faculty of Informatics of Graz University of Technology for dissertation. 2018 ( link)
  • Best Demo Honourable Mention at the 15th International Conference on Knowledge Technologies and Data-Driven Business (i-Know’2015) in Graz, Austria. 2015 ( link)
  • Best Poster Award at the 25th ACM Conference on Hypertext and Social Media (HT’2014) in Santiago, Chile. 2014 ( link)

Grants

  • Intern Grant for Female Internship, FFG Femtech (2025), 8.5k for Fair-AI, Know-Center GmbH as co-supervisor. ( link)
  • Project Grant for LUMEN, Horizon Europe (2024 - 2027), 415k for Know-Center (83k for FAIR-AI) as key researcher ( link)
  • Project Grant for TILDE (Trustworthy Access to Knowledge from the Indexed Web), OpenWebSearch.eu Third-Party Call#2 (2024 - 2025), 100k for the Know-Center (33k for FAIR-AI) as key researcher ( link)
  • Project Grant for FairRecSys, SFG - Zukunftsfonds Steiermark (2024 - 2025), 74k for TU Graz, ISDS (37k for FAIR-AI) as key researcher ( link)
  • Project Grant for SAIROM, FFG - AI4Green (2024 - 2025), 50k for the Know-Center (25k for FAIR-AI) as key researcher ( link)
  • FFG – K1 Research Center Grant, 20.4M for first period (2023 - 2026) of the Know-Center (3.4M for Fair-AI) as research area manager for Fair-AI ( link)
  • FFG - COMET Module Grant, 3,7M for "DDIA – Data Driven Immersive Analytics in Digital Industries" (2022 - 2026) for the Know-Center (350k for Social Computing) as key researcher for sub-project "Personalized Immersive Learning Support" ( link)
  • Project Grant for Radreisen4All, FFG Femtech (2022 - 2025), 150k for Fair-AI, Know-Center GmbH as key researcher. ( link)
  • Intern Grant for Female Internship, FFG Femtech (2022 - 2023), 8.5k for Fair-AI, Know-Center GmbH as co-supervisor. ( link)
  • Travel Grant by Land Steiermark for research stay for 1 week (2021) at XAI Group of Maastricht University, The Netherlands. ( link)
  • Travel Grant by Land Steiermark for research stay for 1 week (2020) at WIS Group of TU Delft, The Netherlands (postponed due to COVID-19). ( link)
  • Project Grant JOLIOO, FFG Basisantrag (2020), 120k for Social Computing, Know-Center GmbH as researcher. ( link)
  • Project Grant for COGSTEPS, Erasmus+ (2020 - 2023), 130k for the Know-Center and ISDS@TU-Graz as researcher. ( link)
  • Project Grant for TRUSTS, H2020 (2020 - 2022), 730k for the Know-Center (138k for Social Computing) as task leader. ( link)
  • FFG - COMET Module Grant, 3,7M for "DDAI – Explainable, Verifiable and Privacy-Preserving Data-Driven AI" (2020 - 2023) for the Know-Center (700k for Social Computing) as key researcher for sub-project "Explainable AI for Users" ( link)
  • Project Grant for TRIPLE, H2020 (2019 - 2022), 377k for the Know-Center (120k for Social Computing) as researcher. ( link)
  • Travel Grant for the European Symposium on Computational Social Science (EUROCSS'2019) in Zurich, Switzerland. ( link)
  • FFG – K1 Research Center Grant, 20.4M for second funding period (2019 - 2022) of the Know-Center (3.4M for Social Computing) as deputy research head head for Social Computing ( link)
  • Project Grant for AI4EU, H2020 (2019 - 2021), 147k for the Know-Center (73.5k for Social Computing) as co-task leader. ( link)
  • Travel Grant for the European Symposium on Computational Social Science (EUROCSS'2018) in Cologne, Germany. ( link)
  • Project Grant for OpenAIRE Matchmaker: An innovative recommendation service for finding new scientific collaborators, OpenAIRE Open Tender Calls LOT II Value-added services (2018), 15k for Social Computing, Know-Center as researcher. ( link)
  • Project Grant for Health-Literacy und Diversity fuer SchuelerInnen der Sekundaerstufe I (Heli-D), SFG - Gesundheitsfonds Steiermark (2018 - 2021), 75k for the Know-Center (37.5k for Social Computing) as WP-leader. ( link)
  • Travel Grant for the 27th ACM Conference on Hypertext and Social Media (HT'2016) in Halifax, Canada. ( link)
  • Project Grant for Data Market Austria (DMA), IKT der Zukunft (2015 - 2019), 286k for the Know-Center (170k for Social Computing) as researcher. ( link)

Selected Software projects, white papers, and datasets

  • Trustworthy AI, White Papers on Trustworthy AI Requirements. ( link)
  • TagRec, Towards A Standardized Tag Recommender Benchmarking Framework. ( link)
  • FairRecSys, Python scripts for studying bias in recommender systems. ( link)
  • FairRecSys Datasets, Datasets for studying bias in recommender systems. ( link)
  • Calibration Datasets, Datasets for studying calibration in recommender systems. ( link)
  • MetaMF User Groups, User Groups for Robustness of Meta Matrix Factorization Against Decreasing Privacy Budgets. ( link)
  • RobustnessOfMetaMF, Scripts for analyzing the robustness of MetaMF for privacy-aware recommmendations. ( link)
  • LFM-BeyMS, Beyond-mainstream music in the LFM-1b dataset. ( link)
  • SupportTheUnderground, Scripts for analyzing beyond-mainstream music listeners in the LFM-1b dataset. ( link)
  • LFM User Groups, User groups of the LFM-1b dataset. ( link)
  • ScaR, Scalable Recommendation-as-a-Service. ( link)
  • Layers, The Learning Layers Technical Infrastructure. ( link)


Contact

dkowald [AT] know [MINUS] center [DOT] at / dominik.kowald [AT] gmail [DOT] com

+43 (316) 873-30846

dkowald1

Dominik Kowald

Dominik Kowald, Know-Center GmbH, Sandgasse 36/4, 8010 Graz, Austria