Irina Nalis-Neuner

Projektass.in Mag.a rer.nat. Dr.in rer.nat.

Role

PostDoc Researcher
Data Science, E194-04

Contact

Publications

Like a Skilled DJ - an Expert Study on News Recommendations Beyond Accuracy / 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)
News Diversity and Well-Being – An Experimental Exploration Of Diversity-Aware Recommender Systems / Basso, L., Nalis-Neuner, I., & Neidhardt, J. (2023). News Diversity and Well-Being – An Experimental Exploration Of Diversity-Aware Recommender Systems. In FAccTRec Program. 6th FAccTRec Workshop on Responsible Recommendation at RecSys 2023, Singapur, Singapore.
Project: CDL-RecSys (2022–2028)
2nd ACM Digital Humanism Summer School / Nalis-Neuner, I. (2023, September). 2nd ACM Digital Humanism Summer School [Keynote Presentation]. 2nd ACM Digital Humanism Summer School, Wien, Austria.
Project: CDL-RecSys (2022–2028)
Not Facial Expression, nor Fingerprint – Acknowledging Complexity and Context in Emotion Research for Human-Centered Personalization and Adaptation / Nalis, I., & Neidhardt, J. (2023). Not Facial Expression, nor Fingerprint – Acknowledging Complexity and Context in Emotion Research for Human-Centered Personalization and Adaptation. In Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (pp. 325–330). Association for Computing Machinery. https://doi.org/10.1145/3563359.3596990
Project: CDL-RecSys (2022–2028)
Sentiment Analysis - psychological perspective / Nalis-Neuner, I. (2023, January 25). Sentiment Analysis - psychological perspective [Presentation]. ÖAW Winter School 2023, Wien, Austria.
Project: CDL-RecSys (2022–2028)
Recommender Systems: Techniques, Effects, and Measures Toward Pluralism and Fairness / Knees, P., Neidhardt, J., & Nalis-Neuner, I. (2023). Recommender Systems: Techniques, Effects, and Measures Toward Pluralism and Fairness. In H. Werthner, C. Ghezzi, & J. Kramer (Eds.), Introduction to Digital Humanism : A Textbook (pp. 417–434). Springer. https://doi.org/10.1007/978-3-031-45304-5_27
Projects: CDL-RecSys (2022–2028) / HumRec (2021–2025)
The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic / 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)