About me

Dominik Kowald

Dr. Dominik Kowald is research area manager of the Social Computing team at the Know-Center, Austria's leading research center for data-driven business and big data analytics. Additionally, he is senior researcher at the Institute of Interactive Systems and Data Science of Graz University of Technology. He has a PhD. (with hons), MSc. (with hons) and BSc. in Computer Science from Graz University of Technology. He has finished his PhD in October 2017 in the course of the European-funded research project Learning Layers on cognitive-inspired recommender systems for social tagging and microblogging environments. Currently he is working as key researcher in the DDAI and DDIA COMET modules, and as task lead in the H2020 TRUSTS project. He is review editor of Frontiers in Big Data - Recommender Systems section, and his research interests are in the fields of recommender systems, privacy, fairness and biases in algorithms, Web science and computational social science.
Full cv: ( .pdf)
Teaching certificate: ( .pdf)
Dissertation summary: ( .pdf) ( slides)

Selected publications (last 5 years)

  • Lacic, E., Fadljevic, L., Weissenboeck, F., Lindstaedt, S., & Kowald, D. (2022). What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations. In Proceedings of the 44th European Conference on Information Retrieval (ECIR'2022). Springer. ( .pdf)
  • Kowald, D., Muellner, P., Zangerle, E., Bauer, C., Schedl, M. & Lex, E. (2021). Support the Underground: Characteristics of Beyond-Mainstream Music Listeners. EPJ Data Science. ( .pdf) ( blog) ( news)
  • Lex, E., Kowald, D., Seitlinger, P., Tran, T., Felfernig, A., & Schedl, M. (2021). Psychology-informed Recommender Systems. Foundations and Trends in Information Retrieval, Vol. 15, No. 2. ( .pdf)
  • Kowald, D., Schedl, M., & Lex, E. (2020). The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study. In Proceedings of the 42nd European Conference on Information Retrieval (ECIR'2020). Springer. ( .pdf)
  • Kowald, D., Pujari, S., & Lex, E. (2017). Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach. In Proceedings of the 26th International World Wide Web Conference (WWW'2017). ACM. ( .pdf)


Publications

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

Journal articles and book contributions

  1. 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)
  2. 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)
  3. 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)
  4. 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)
  5. 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)
  6. 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)
  7. Hasani-Mavriqi, I., Kowald, D., Helic, D., & Lex, E. (2018). Consensus Dynamics in Online Collaboration Systems. Computational Social Networks Journal. SpringerOpen. ( .pdf)
  8. 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)
  9. 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)
  10. 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)
  11. 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)
  12. 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)
  13. 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. In Mining, Modeling, and Recommending'Things' in Social Media (pp. 55-74). Springer International Publishing. ( .pdf)
  14. 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. In Mining, Modeling, and Recommending'Things' in Social Media (pp. 75-95). Springer International Publishing. ( .pdf)
  15. 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. In Mining, Modeling, and Recommending'Things' in Social Media (pp. 96-115). Springer International Publishing. ( .pdf)

Conference and workshop contributions

  1. 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)
  2. Kowald, D., & Lacic, E. (2022). Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems. In Proceedings of the BIAS Workshop co-located with the 44th European Conference on Information Retrieval (ECIR'2022). Springer. ( .pdf)
  3. 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)
  4. 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)
  5. 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)
  6. 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)
  7. 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)
  8. 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)
  9. 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)
  10. 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)
  11. 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)
  12. 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)
  13. 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)
  14. 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)
  15. 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)
  16. 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)
  17. 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)
  18. 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)
  19. 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)
  20. 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)
  21. 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)
  22. 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)
  23. 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)
  24. 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)
  25. 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)
  26. Kowald, D., & Lex, E. (2018). Studying Confirmation Bias in Hashtag Usage on Twitter. European Symposium on Computational Social Science (EUROCSS'2018). ( .pdf) ( poster)
  27. 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)
  28. 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)
  29. 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)
  30. 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)
  31. 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)
  32. 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)
  33. 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)
  34. 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)
  35. 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)
  36. 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)
  37. 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)
  38. 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)
  39. 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)
  40. 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)
  41. 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)
  42. 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)
  43. 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)
  44. 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)
  45. 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)
  46. 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)
  47. 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)
  48. 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)
  49. 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)
  50. 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)
  51. 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)

Other publications and theses

  1. 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)
  2. 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)
  3. Muellner, P., Lex, E., & Kowald, D. (2021). Impact of Meta Learning for Privacy-Preserving Recommender Systems. In The Responsible AI Forum (TRAIF'2021). ( .pdf)
  4. 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)
  5. Kowald, D. (2017). Modeling Activation Processes in Human Memory to Improve Tag Recommendations. PhD thesis, Graz University of Technology. ( .pdf)
  6. 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)
  7. 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)
  8. 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)
  9. 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)
  10. 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)
  11. 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

Session chairing and workshop organization

  • 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'2022, 16th ACM Conference on Recommender systems, 2022 ( link)
  • PERSPECTIVES'2022, Perspectives on the Evaluation of Recommender Systems co-located with RecSys'2022, 2022 ( link)
  • ICHCI, International Journal of Human–Computer Interaction, 2022 ( link)
  • UMAP'2022-DC, 30th Conference on User Modeling, Adaptation and Personalization - Doctoral Consortium, 2022 ( link)
  • CIKM'2022, 31th ACM International Conference on Information and Knowledge Management (short paper track), 2022 ( link)
  • ECTEL'2022 (leading reviewer), 17th European Conference on Technology-Enhanced Learning, 2022 ( link)
  • HT'2022, 33red ACM Conference on Hypertext and Social Media, 2022 ( link)
  • UMAP'2022, 30th Conference on User Modeling, Adaptation and Personalization, 2022 ( link)
  • HAAPIE'2022, 7th International Workshop on Human Aspects in Adaptive and Personalized Interactive Environments (HAAPIE) co-located with the 30th Conference on User Modeling, Adaptation and Personalization (UMAP), 2022 ( link)
  • ICWE'2022, 22nd International Conference on Web Engineering, 2022 ( link)
  • IUI'2022, 26th ACM Conference on Intelligent User Interfaces, 2022 ( link)
  • FRONTIERS (review editor), Frontiers in Big Data - Section Recommender Systems, 2021 ( link)
  • TIST, ACM Transactions on Intelligent Systems and Technology, 2021 ( link)
  • MORS'2021, Workshop on Multi-Objective Recommender Systems co-located with RecSys'2021, 2021 ( link)
  • PERSPECTIVES'2021, Perspectives on the Evaluation of Recommender Systems co-located with RecSys'2021, 2021 ( link)
  • CIKM'2021, 30th ACM International Conference on Information and Knowledge Management (resource track), 2021 ( link)
  • RecSys'2021, 15th ACM Conference on Recommender systems (Reproducibility track), 2021 ( link)
  • ASC, Applied Soft Computing, 2021 ( link)
  • ECTEL'2021 (leading reviewer), 16th European Conference on Technology-Enhanced Learning, 2021 ( link)
  • HT'2021, 32nd ACM Conference on Hypertext and Social Media, 2021 ( link)
  • UMAP'2021, 29th Conference on User Modeling, Adaptation and Personalization, 2021 ( link)
  • IUI'2021, 25th ACM Conference on Intelligent User Interfaces, 2021 ( link)
  • ECTEL'2020 (leading reviewer), 15th European Conference on Technology-Enhanced Learning, 2020 ( link)
  • CIKM'2020, 29th ACM International Conference on Information and Knowledge Management (resource track), 2020 ( link)
  • FRONTIERS, Frontiers in Psychology, 2020 ( link)
  • RecSys'2020, 14th ACM Conference on Recommender systems (LBR and reproducibility tracks), 2020 ( link)
  • HT'2020, 31st ACM Conference on Hypertext and Social Media, 2020 ( link)
  • TheWebConf'2020, International World Wide Web Conference (poster track), 2020 ( link)
  • IUI'2020, 25th ACM Conference on Intelligent User Interfaces, 2020 ( link)
  • RDSM'2020, 3rd International Workshop on Rumours and Deception in Social Media co-located with COLING'2020 conference, 2020 ( link)
  • EPJ, EPJ Data Science, 2019 ( link)
  • TWEB, ACM Transactions on the Web, 2019 ( link)
  • HT'2019, 30th ACM Conference on Hypertext and Social Media, 2019 ( link)
  • WebSci'2019, 11th ACM Conference on Web Science, 2019 ( link)
  • EUROCSS'2019, Third European Symposium on Computational Social Science in Zurich, Switzerland ( link)
  • TCSC, IEEE Transactions on Computational Social Systems, 2019 ( link)
  • ASC, Applied Soft Computing, 2019 ( link)
  • MSM'2019, 10th International Workshop on Modeling Social Media - Mining, Modeling and Learning from Social Media co-located with WWW’2019 conference, San Francisco, USA, 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'2018, 12th ACM Conference on Recommender Systems (poster track), Vancouver, Canada, 2018 ( link)
  • UMAP'2018, 26th Conference on User Modeling, Adaptation and Personalization, Singapore, Singapore, 2018 ( link)
  • SoAPS'2018, Workshop on Social Aspects in Personalization and Search co-located with ECIR'2018 conference, Grenoble, France, 2018 ( link)
  • AFEL'2018, Analytics for Everyday Learning Workshop co-located with ECTEL'2018 conference, Leeds, UK, 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'2017, The Fourth International Symposium on Social Networks Analysis, Management and Security, Prague, Czech Rebublic ( link)
  • WebSci'2017, 9th International ACM Web Science Conference (poster track), Troy, New York, 2017 ( link)
  • OpenSym'2017, 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'2015, 6th International Workshop on Modeling Social Media - Behavioral Analytics in Social Media, Big Data and the Web co-located with WWW’2015 conference, Florence, Italy, 2015 ( link)
  • UMAP'2014, 22nd Conference on User Modelling, Adaption and Personalization, Aalborg, Denmark, 2014 ( link)
  • EC-TEL'2014, 9th European Conference on Technology Enhanced Learning, Graz, Austria, 2014 ( link)

Presentations at international conferences and events

  • FH'2022, FH Joanneum, Journalism course, Summer-term 2022, invited external lecture on recommender systems in media and beyond ( 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. ( 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

  • Dissertation Award by Arbeiterkammer Steiermark, Austria. ( 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. ( link)
  • Nominated for Heinz Zemanek Award by Faculty of Informatics of Graz University of Technology for dissertation. ( link)
  • Best Demo Honourable Mention at the 15th International Conference on Knowledge Technologies and Data-Driven Business (i-Know’2015) in Graz, Austria. ( link)
  • Best Poster Award at the 25th ACM Conference on Hypertext and Social Media (HT’2014) in Santiago, Chile. ( link)

Grants

  • FFG – K1 Research Center Grant, 20.4M for first period (2013 - 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 - 2024), 150k for Social Computing, Know-Center GmbH as key researcher. ( 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 - 2022) 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 co-writer ( 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), 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)

Co-supervised theses

  • Mayr, G. (2022). Accuarcy, Popularity Lift and Calibration in Recommender Systems. Master project.. Graz University of Technology. (to be published)
  • Mayr, G. (2022). Calibration in Recommender Systems. Bachelor thesis.. Graz University of Technology. ( .pdf)
  • Duricic, T. (2020 - 2024). Sparsity and Interpretability of Graph-based Recommender Systems. Doctoral thesis. Graz University of Technology. (to be published in 2024)
  • Muellner, P. (2020 - 2023). Privacy in Recommender Systems. Doctoral thesis. Graz University of Technology. (to be published in 2023)
  • Wagner, M. (2020). Diversity-Aware Recommendation of Tweets. Master thesis. Graz University of Technology. ( .pdf)
  • Muellner, P. (2019). Studying Non-Mainstream Music Listening Behavior For Fair Music Recommendations. Master thesis. Graz University of Technology. ( .pdf)
  • Punz, A. (2016). Detection and Analysis of Communities on Twitter. Bachelor thesis. Graz University of Technology. ( .pdf)

Software projects and datasets

  • 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)
  • 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)
  • SNA, Computational Social Networks and Demand Forecasting. ( 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, Inffeldgasse 13/6, 8010 Graz, Austria