Thomas Elmar Kolb
Projektass. Dipl.-Ing. / BSc
Roles
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PreDoc Researcher
Data Science, E194-04 -
Curriculum Commission for Business Informatics
Substitute Member
Contact
- thomas.kolb@tuwien.ac.at
- +43-1-58801-49590
- Favoritenstrasse 9, Room HD0111
- vCard from TISS
Courses
2024S
- Advanced Topics in Recommender Systems and Generative AI / 194.164 / SE
- Recommender Systems / 194.035 / VU
Publications
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Like a Skilled DJ - an Expert Study on News Recommendations Beyond Accuracy
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Kolb, T. E., Nalis-Neuner, I., & Neidhardt, J. (2023). Like a Skilled DJ - an Expert Study on News Recommendations Beyond Accuracy. In B. Kille (Ed.), Proceedings of the International Workshop on News Recommendation and Analytics co-located with the 2023 ACM Conference on Recommender Systems (RecSys 2023). CEUR-WS.org. https://doi.org/10.34726/5332
Download: PDF (530 KB)
Project: CDL-RecSys (2022–2028) -
Potentials of Combining Local Knowledge and LLMs for Recommender Systems
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Kolb, T. E., Wagne, A., Sertkan, M., & Neidhardt, J. (2023). Potentials of Combining Local Knowledge and LLMs for Recommender Systems. In V. W. Anelli, P. Basile, G. De Melo, F. Donini, A. Ferrara, C. Musto, F. Narducci, A. Ragone, & M. Zanker (Eds.), Proceedings of the Fifth Knowledge-aware and Conversational Recommender Systems Workshop co-located with 17th ACM Conference on Recommender Systems (RecSys 2023) (pp. 61–64). CEUR-WS.org. https://doi.org/10.34726/5334
Download: PDF (317 KB)
Project: CDL-RecSys (2022–2028) -
The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic
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Kolb, T. E., Nalis, I., Sertkan, M., & Neidhardt, J. (2022). The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic. In Kolb Thomas (Ed.), Unofficial Proceedings of the 5th FAccTRec Workshop on Responsible Recommendation at RecSys 2022. https://doi.org/10.48550/ARXIV.2209.07608
Project: CDL-RecSys (2022–2028) -
The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers
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Kolb, T. E., Sekanina, K., Kern, B. M. J., Neidhardt, J., Wissik, T., & Baumann, A. (2022). The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers. In LREC 2022 Conference Proceedings (pp. 4708–4716). European Language Resources Association. https://doi.org/10.34726/4101
Download: PDF (604 KB)
Project: DYSEN (2020–2021) -
Dynamic sentiment analysis for measuring media bias
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Kolb, T. E. (2022). Dynamic sentiment analysis for measuring media bias [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.73300
Download: PDF (1.15 MB) -
Creating an Austrian language polarity dictionary with the crowd
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Kolb, T. E., Sekanina, K., Kern, B. M. J., Neidhardt, J., Baumann, A., & Wissik, T. (2021, December 11). Creating an Austrian language polarity dictionary with the crowd [Conference Presentation]. Workshop: “Österreichisches Treffen zu Sentimentinferenz (ÖTSI)” der Österreichischen Linguistik-Tagung 2021, Austria. http://hdl.handle.net/20.500.12708/153831
Download: Slides of the Presentation (1.08 MB)
Project: DYSEN (2020–2021) - A review and cluster analysis of German polarity resources for sentiment analysis / Kern, B., Baumann, A., Kolb, T., Sekanina, K., Hofmann, K., Wissik, T., & Neidhardt, J. (2021). A review and cluster analysis of German polarity resources for sentiment analysis. In 3rd Conference on Language, Data and Knowledge (LDK 2021) (pp. 1–17). OASICS. https://doi.org/10.4230/OASIcs.LDK.2021.37
Supervisions
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COVID-19 and populism in Austrian news user comments - A machine learning approach
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Wagne, A. (2023). COVID-19 and populism in Austrian news user comments - A machine learning approach [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.105940
Download: PDF (1.84 MB) -
Exploring group fairness in news media recommendations: Algorithms, metrics, and grouping
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Huebner, B. (2023). Exploring group fairness in news media recommendations: Algorithms, metrics, and grouping [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.107255
Download: PDF (870 KB)