About me

Univ.-Prof. Dr. Dominik Kowald

I am Professor for AI-based Information Retrieval in Digital Humanities (25%) at the University of Graz as well as head of the research area FAIR-AI at the Know Center, one of Europe's leading research centers for trustworthy AI. I hold a venia docendi (habilitation) and a Ph.D. with distinction in Applied Computer Science from TU Graz. In my Ph.D. thesis, I utilized the cognitive architecture ACT-R to model human word retrieval for Natural Language Processing and Machine Learning tasks, such as modeling and predicting hashtag usage. During my post-doctoral research on trustworthy AI and recommender systems, I was a recipient of the Styrian Provincial Government mobility grant for young scientists for a research visit at the XAI group at Maastricht University. Currently, I am key researcher in the international 3.7M€ Interfaces of Agent-Centric AI COMET module, as part of which I will conduct a research stay at the TU Munich Institute for Ethics in AI in summer 2027. I have published more than 130 papers in interdisciplinary and computer science venues, leading to invitations at renowned international institutions, including Schloss Dagstuhl, MediaFutures Bergen, and the ELLIS Unit Berlin.

Research fields: Information Retrieval; Recommender Systems; NLP; Trustworthy AI; Machine Learning; Digital Humanities

Open theses: ( link)

Key Achievements

Full list available in my CV: ( .pdf)

Selected publications:

  • Atzenhofer-Baumgartner, F., & Kowald, D. (2026). What Do Humanities Scholars Need? A User Model for Recommendation in Digital Archives. In Proceedings of UMAP'2026. ( .pdf)
  • 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. International Journal on Human Computer Studies. ( .pdf)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)

Research

Teaching

Management



Publications

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

Journal articles (incl. editorials)

  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. International Journal on Human Computer Studies. ( .pdf)
  2. 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)
  3. Muellner, P., Schreuer, A., Kopeinik, S., Wieser, B., & Kowald, D., (2025). Multistakeholder Fairness in Tourism: What can Algorithms learn from Tourism Management? Frontiers in Big Data. ( .pdf)
  4. Demelius, L., Kowald, D., Kopeinik, S., Kern, R., & Trügler, A. (2025). Private and Fair Machine Learning: Revisiting the Disparate Impact of Differentially Private SGD. TMLR. ( .pdf)
  5. Forster, V., Fleiß, J., Kowald, D., & Robertson, V. (2025). Detecting Resale Price Maintenance with Unsupervised Machine Learning. Journal of Competition Law and Economics. ( .pdf)
  6. 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 ( .pdf)
  7. 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)
  8. Kowald, D., Yang, D., & Lacic, E. (2024). Editorial: Reviews in Recommender Systems. Frontiers in Big Data. ( .pdf)
  9. 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)
  10. 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)
  11. 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)
  12. 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)
  13. 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)
  14. 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)
  15. 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)
  16. 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)
  17. 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)
  18. 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)
  19. Hasani-Mavriqi, I., Kowald, D., Helic, D., & Lex, E. (2018). Consensus Dynamics in Online Collaboration Systems. Computational Social Networks Journal. SpringerOpen. ( .pdf)
  20. 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)
  21. 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)
  22. 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)
  23. 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. Atzenhofer-Baumgartner, F., & Kowald, D. (2026). What Do Humanities Scholars Need? A User Model for Recommendation in Digital Archives. In Proceedings of UMAP'2026. ( .pdf)
  2. Ungruh, R., Bellogin, A., Kowald, D., & Pera, S. (2026). When Preference is not Enough: Why Recommender Systems Require Human-Aware Evaluation for Children. In Proceedings of UMAP'2026. ( .pdf)
  3. Muellner, P., Kowald, D., Schedl, M., & Lex, E. (2026). Meta-Learning and Targeted Differential Privacy to Improve the Accuracy–Privacy Trade–off in Recommendations. In Proceedings of UMAP'2026. ( .pdf)
  4. Forster, A., Muellner, P., Helic, D., Lex, E., & Kowald, D. (2026). Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems. In Proceedings of the LLM4Good Workshop co-located with UMAP'2026. ( .pdf)
  5. Forster, A., Autischer, G., Kowald, D.,, & Kopeinik, S. From Assessment to Aggregation: A Multistakeholder Framework for Identifying and Mitigating Biases in AI-Driven Job Profiling and Recommendation. In Proceedings of BIAS'2026. ( .pdf)
  6. Rejc, A., Madritsch, J., Elsayed, N., Kowald, D.,, & Kopeinik, S. An Investigation of Trust and Overreliance on Conversational Instructions in Augmented Reality. In Proceedings of CUI'2026. ( .pdf)
  7. Autischer, G., Waxnegger, K., & Kowald, D.. (2026). Towards EU AI Act Compliance: Self-Certification and Fairness Alignment for Facial Emotion Recognition. In Proceedings of ECAF'2026 ( .pdf)
  8. Atzenhofer-Baumgartner, F., Vogeler, G., & Kowald, D. (2025). A Multistakeholder Approach to Value-Driven Co-Design of Recommender System Evaluation Metrics in Digital Archives. In Proceedings of RecSys'2025. ( .pdf)
  9. Forster, A., Kopeinik, S., Helic, D., Thalmann, S. & Kowald, D. (2025). Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations. In Proceedings of RecSys'2025. ( .pdf)
  10. Ungruh, R., Bellogin, A., Kowald, D., & Pera, S. (2025). Impacts of Mainstream-Driven Algorithms on Recommendations for Children Across Domains: A Reproducibility Study. In Proceedings of RecSys'2025. ( .pdf)
  11. 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)
  12. 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)
  13. 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)
  14. 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)
  15. 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)
  16. 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)
  17. 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)
  18. 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)
  19. 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)
  20. 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)
  21. 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)
  22. 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)
  23. 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)
  24. 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)
  25. 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)
  26. 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)
  27. 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)
  28. 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)
  29. 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)
  30. 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)
  31. 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)
  32. 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)
  33. Muellner, P., Lex, E., & Kowald, D. (2021). Impact of Meta Learning for Privacy-Preserving Recommender Systems. In The Responsible AI Forum (TRAIF'2021). ( .pdf)
  34. 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)
  35. 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)
  36. 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)
  37. 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)
  38. 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)
  39. 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)
  40. 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)
  41. 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)
  42. 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)
  43. 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)
  44. 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)
  45. 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)
  46. 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)
  47. 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)
  48. 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)
  49. 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)
  50. 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)
  51. 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)
  52. 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)
  53. 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)
  54. Kowald, D., & Lex, E. (2018). Studying Confirmation Bias in Hashtag Usage on Twitter. European Symposium on Computational Social Science (EUROCSS'2018). ( .pdf) ( poster)
  55. 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)
  56. 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)
  57. 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)
  58. 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)
  59. 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)
  60. 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)
  61. 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)
  62. 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)
  63. 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)
  64. 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)
  65. 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)
  66. 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)
  67. 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)
  68. 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)
  69. 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)
  70. 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)
  71. 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)
  72. 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)
  73. 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)
  74. 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)
  75. 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)
  76. 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)
  77. 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)
  78. 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)
  79. 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)

Other publications and theses

  1. Autischer, G., Waxnegger, K., & Kowald, D.. (2026). Self-Certification of High-Risk AI Systems: The Example of AI-based Facial Emotion Recognition. Preprint. ( .pdf)
  2. Lesota, O., Escobeda, G., Ferwerda, B., Kopeinik, S., Kowald, D., & Schedl, M. (2026). Robustness and user-perceived value of popularity calibration in musicrecommendation: A user study. In revision in TORS. ( .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. 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)
  5. Kowald, D. (2024). Transparency, Privacy, and Fairness in Recommender Systems. Habilitation (post-doctoral thesis), Graz University of Technology. ( .pdf)
  6. 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)
  7. Semmelrock, H., Kopeinik, S., Theiler, D., Ross-Hellauer, T., & Kowald, D. (2023). Reproducibility in Machine Learning-Driven Research. Preprint. ( .pdf)
  8. 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)
  9. 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)
  10. Kowald, D. (2017). Modeling Activation Processes in Human Memory to Improve Tag Recommendations. PhD thesis, Graz University of Technology. ( .pdf) ( summary) ( slides)
  11. 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)
  12. 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)
  13. 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)
  14. 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 event organization and session chairing

  • IUI'2027, Co-organizer of the HumaneLLM Workshop co-located with IUI'2027, Helsinki, Finland, 2027 ( link)
  • UMAP'2026 DC, Mentor in the Doctoral Consortium of UMAP 2026, Gothenburg, Sweden, 2026 ( link)
  • STS'2026, Co-Organizer of the Fairness and Artificial Intelligence workshop at the STS conference, Graz, Austria, 2026 ( link)
  • DARIAH'2026, Co-Organizer of the Efficient Similarity Hackathon for Advanced Search in Cultural Heritage, Graz, Austria, 2026 ( link)
  • 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) ( policy) ( discussion)
  • STS'2025, Co-Organizer of the Fairness and Artificial Intelligence workshop at the STS conference, Graz, Austria, 2025 ( link)
  • CRBAM'2025, Co-Organizer of the Fair Recommendations for Cyclists workshop at 9th Annual Meeting of the Cycling Research Board, Amsterdam, The Netherlands, 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

  • IUI (senior PC), ACM Conference on Intelligent User Interfaces, since 2027 ( link)
  • SynIRgy, International Workshop on Simulation and Synthetic Data for Information Retrieval co-located with ECIR, since 2026 ( link)
  • RecSys (senior PC), Reproducibility track of the ACM Conference on Recommender Systems, since 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)
  • EC-TEL (senior PC), European Conference on Technology Enhanced Learning, since 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 ( link)

Presentations at international conferences and events

  • UMAP'2026, LBR paper track of UMAP, Gothenburg, Sweden. ( poster)
  • UMAP'2026, LBR paper track of UMAP, Gothenburg, Sweden. ( poster)
  • ELLIS'2026, Invited talk on recommender systems at the ELLIS Unit and BIFOLD Berlin, Germany. ( link)
  • DigHum'2026, Invited talk on recommender systems at the Department of Digital Humanities of University of Graz, Graz, Austria. ( link)
  • SmartReg'2025, Invited panelist on AI regulation at Smart Regulation symposium of University of Graz, Graz, Austria. ( link)
  • RecSys'2025, Reproducibility paper track of the 19th ACM Conference on Recommender Systems, Prague, Czech Republic. ( poster)
  • RecSys'2025, Short paper track of the 19th ACM Conference on Recommender Systems, Prague, Czech Republic. ( poster)
  • RecSys'2025, Short paper track of the 19th ACM Conference on Recommender Systems, Prague, Czech Republic. ( poster)
  • EWAF'2025, Lightning talk at the 4th European Workshop on Algorithmic Fairness, Eindhoven, The Netherlands (with travel grant). ( slides)
  • EWAF'2025, Poster session of the 4th European Workshop on Algorithmic Fairness, Eindhoven, The Netherlands (with travel grant). ( poster)
  • 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, Honours & Mobility Grants

  • RecSys Journal Paper of the Year Award at the ACM Conference on Recommender Systems (RecSys'26). 2026 ( link)
  • Competitive Conference Mobility Grant for traveling to the European Workshop on Algorithmic Fairness in Eindhoven, The Netherlands. 2025 ( link)
  • Mind-the-Gap Gender and Diversity Award by TU Graz, Austria, for the FairRecSys project on multistakeholder fairness. 2025 ( link)
  • Runner-up for Styrian Research Award (final round) by State of Styria, Austria, for my habilitation. 2025 ( link)
  • Outstanding Reviewer Award at the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP'24) in Cagliari, Italy. 2024 ( link)
  • RecSys Journal Paper of the Year Award at the ACM Conference on Recommender Systems (RecSys'23). 2023 ( link)
  • Mind-the-Gap Gender and Diversity Award by TU Graz, Austria, for research on popularity bias in AI-driven multimedia recommender systems. 2022 ( link)
  • Competitive Mobility Grant for Young Scientists by State of Styria for a research visit on explainable AI at Maastricht University, The Netherlands. 2021 ( link)
  • Competitive Mobility Grant for Young Scientists by State of Styria for a research visit on explainable AI at TU Delft, The Netherlands. 2020 ( link)
  • Competitive Conference Mobility Grant for traveling to the Third European Symposium on Computational Social Science (ESCSS) in Zurich, Switzerland. 2019 ( link)
  • Competitive Conference Mobility Grant for traveling to the Second European Symposium on Computational Social Science (ESCSS) in Cologne, Germany. 2018 ( link)
  • Ph.D. Thesis Award by Chamber of Labor Styria (Arbeiterkammer Steiermark), Austria. 2018 ( link)
  • Nominated for ACM SIGCHI Outstanding Dissertation Award by Institute of Interactive Systems and Data Science of TU Graz. 2018 ( link)
  • Nominated for Award of Excellence by Faculty of Informatics of TU Graz for dissertation. 2018 ( link)
  • Nominated for Heinz Zemanek Award by Faculty of Informatics of TU Graz for dissertation. 2018 ( link)
  • Competitive Conference Mobility Grant for traveling to the 27th ACM Conference on Hypertext and Social Media (HT'16) in Halifax, Canada. 2016 ( link)
  • Best Demo Honourable Mention at the 15th International Conference on Knowledge Technologies and Data-Driven Business (i-KNOW'15) in Graz, Austria. 2015 ( link)
  • Best Poster Award at the 25th ACM Conference on Hypertext and Social Media (HT'14) in Santiago, Chile. 2014 ( link)

Research Project Funding

  • Project Grant for REPLICATE, Horizon Europe (2026 - 2029), 360k€ for Know Center (360k€ for FAIR-AI) as study co-lead for AI replications ( link)
  • Project Grant for EU MARitime SECurity through Autonomous Real-time Surveillance platforms (MARSEC), Horizon Europe (2026 - 2029), 200k€ for FAIR-AI at Know Center as task lead on AI ethics ( link)
  • Project Grant for CompreHensive EVALuation methods for GPAI models (CHEVAL), Horizon Europe (2026 - 2029), 580k€ for FAIR-AI at Know Center as co-work package lead on LLM evaluation ( link)
  • Project Grant for Navigational AI assistant for Virtual worlds Immersion (NAVI), Horizon Europe (2026 - 2029), 636k€ for Know Center (506k€ for FAIR-AI) as co-work package lead on LLMs ( link)
  • FFG - COMET Module Grant for Interfaces of Agent-Centric AI (IACAI) (2026 - 2029), 3.7M€ for Know Center (350k€ for FAIR-AI) as key researcher for subproject on societal aspects ( link)
  • Project Grant for CareLLM, FFG BASIS (2026 - 2027), 400k€ for Know Center (200k€ for FAIR-AI) as key researcher for privacy and fairness in LLMs ( link)
  • Project Grant for Linked User-driven Multidisciplinary Exploration Network (LUMEN), Horizon Europe (2025 - 2027), 415k€ for Know Center (83k€ for FAIR-AI) as key researcher for recommendations ( link)
  • Project Grant for AI For a new Design Education Approach (AIDEA), Erasmus+ (2025 - 2027), 51k€ for FAIR-AI at Know Center as key researcher for AI methods in design ( link)
  • Intern Grant for Female Internship, FFG FemTech (2025), 8.5k€ for FAIR-AI at Know Center as supervisor ( link)
  • Project Grant for Pro'K'ress, FFG BASIS (2024 - 2025), 348k€ for Know Center (174k€ for FAIR-AI) as key researcher on privacy-aware machine learning ( link)
  • Project Grant for Trustworthy Access to Knowledge from the Indexed Web (TILDE), OpenWebSearch Third-Party Tender (2024 - 2025), 100k€ for Know Center (33k€ for FAIR-AI) as key researcher on bias mitigation in LLMs ( link)
  • Project Grant for Strategic AI Roadmap for Mobility (SAIROM), FFG AI4Green (2024 - 2025), 50k€ for Know Center (25k€ for FAIR-AI) as key researcher on sustainability aspects ( link)
  • Project Grant for FairRecSys, Styrian AI Future Fund (2024 - 2025), 74k€ for TU Graz (37k€ for FAIR-AI) as work package lead on multistakeholder AI aspects ( link)
  • FFG – K1 Research Center Grant for Know Center - Research Center for Trustworthy AI (2023 - 2026), 3.4M€ for FAIR-AI at Know Center as research area head for FAIR-AI ( link)
  • FFG - COMET Module Grant for Data-Driven Immersive Analytics (DDIA) (2022 - 2026), 3.7M€ for Know Center (350k€ for FAIR-AI) as key researcher for subproject on recommendations ( link)
  • Project Grant for Radreisen4All, FFG FemTech (2022 - 2025), 150k€ for FAIR-AI at Know Center as key researcher in the field of multistakeholder recommender systems ( link)
  • Intern Grant for Female Internship, FFG FemTech (2022 - 2023), 8.5k€ for FAIR-AI at Know Center as supervisor ( link)
  • FFG - COMET Module Grant for Data-Driven Artificial Intelligence (DDAI) (2020 - 2023), 3.7M€ for Know Center (700k€ for FAIR-AI) as key researcher for subproject on XAI and private AI ( link)
  • Project Grant for Cogsteps, Erasmus+ (2020 - 2023), 130k€ for FAIR-AI at Know Center and TU Graz as key researcher on recommender systems for matchmaking ( link)
  • Project Grant for Trusted Secure Data Sharing Space (TRUSTS), H2020 (2020 - 2022), 730k€ for Know Center (138k€ for Social Computing) as task lead on AI-based matchmaking ( link)
  • Project Grant for TRIPLE, H2020 (2020 - 2022), 377k€ for Know Center (120k€ for Social Computing) as co-task lead on scholarly recommender systems ( link)
  • Project Grant for AI4EU, H2020 (2020 - 2022), 147k€ for Know Center (73.5k€ for Social Computing) as co-task lead on European data for AI ( link)
  • FFG – K1 Research Center Grant for Know Center - Research Center for Big Data Analytics (2019 - 2022), 3.4M€ for Social Computing at Know Center as deputy research area head ( link)
  • Project Grant for Automated Marketing and Loyalty System for Retailers and Stores (Jolioo), FFG BASIS (2019 - 2021), 120k€ for Social Computing at Know Center as researcher for recommender systems ( link)
  • Project Grant for Studo App Sales Offensive, FFG BASIS (2018 - 2020), 120k€ for Social Computing at Know Center as researcher for recommender systems ( link)
  • Project Grant for Health-Literacy und Diversity (HeLi-D), Styrian Health Fund (2018 - 2020), 75k€ for Know Center (37.5k€ for Social Computing) as work package lead on personalization ( link)
  • Project Grant for OpenAIRE Matchmaker, OpenAIRE Third-Party Tender (2018 - 2020), 75k€ for Know Center as researcher for matchmaking algorithms ( link)
  • Project Grant for Data Market Austria (DMA), FFG Lighthouse (2015 - 2018), 286k€ for Know Center (170k€ for Social Computing) as researcher for recommender systems ( link)

Selected media reactions and scientific dissemination to the public

Membership in professional associations

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

+43-664-6191718

Univ.-Prof. Dr. Dominik Kowald

Univ.-Prof. Dr. Dominik Kowald, University of Graz and Know Center Research GmbH, Sandgasse 34/2, 8010 Graz, Austria