About Science
In August 2021, I embarked on my current role as a PhD Researcher in Norway at the University of Bergen. Since then, my research journey in the field of Recommender Systems has taken flight, focusing on critical issues such as fairness, diversity, biases, and responsibility within recommendations. I am actively contributing to this work as a member of the DARS research group and the MediaFutures research center, all while benefiting from the guidance of accomplished scientists and professors.
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It has been brought to attention more frequently of late that RSs might have certain underlying undesired effects. Notable examples of such phenomena are Popularity Bias, Filter Bubble, and Echo Chamber.
In my PhD project, I am currently focusing on investigating Popularity Bias and ways to evaluate and mitigate its effects.
Recommender Systems is a sub-field of AI - it is a widely used data-driven tool to help us in decision-making in various parts of our day-to-day lives. With the rapid growth of media content online, Recommender Systems have become an essential tool for supporting media users when making choices on which media content to consume.
Publications
Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study. Klimashevskaia, A., Elahi, M., Jannach, D., Skjærven, L., Tessem, A., & Trattner, C. (2023, September). In Proceedings of the 17th ACM Conference on Recommender Systems (pp. 1084-1089).
Addressing Popularity Bias in Recommender Systems: An Exploration of Self-Supervised Learning Models. Klimashevskaia, A., Elahi, M., & Trattner, C. (2023, June). In Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (pp. 7-11).Chicago
Mitigating popularity bias in recommendation: Potential and limits of calibration approaches. Klimashevskaia, A., Elahi, M., Jannach, D., Trattner, C., & Skjærven, L. (2022, April). In International Workshop on Algorithmic Bias in Search and Recommendation (pp. 82-90). Cham: Springer International Publishing.
Popularity bias as ethical and technical issue in recommendation: A survey. Klimashevskaia, A. (2022). In Norsk IKT-konferanse for forskning og utdanning (No. 1).
Comparing the environmental impacts of recipes from four different recipe databases using Natural Language Processing. Reynolds, C., Takacs, B., Klimashevskaia, A., Angelsen, A., Martin, R. I., Brewer, S., ... & Trattner, C. (2021). In LEAP Conference 2021.
Automatic news article generation from legislative proceedings: A phenom-based approach. Klimashevskaia, A., Gadgil, R., Gerrity, T., Khosmood, F., Gütl, C., & Howe, P. (2021, October). In International Conference on Statistical Language and Speech Processing (pp. 15-26). Cham: Springer International Publishing.
To be or not to be central"-On the Stability of Network Centrality Measures in Shakespeare's" Hamlet. Klimashevskaia, A., Geiger, B. C., Hagmüller, M., Helic, D., & Fischer, F. (2020). In 15th Annual International Conference of the Alliance of Digital Humanities Organizations.
A survey on popularity bias in recommender systems. Klimashevskaia, A., Jannach, D., Elahi, M., & Trattner, C. (2024). User Modeling and User-Adapted Interaction, 1-58.
Empowering Editors: How Automated Recommendations Support Editorial Article Curation. Klimashevskaia, A., Elahi, M,. Jannach, D., Trattner, C., & Buodd, S. (2024) In INRA@ RecSys.
Previous Work Experience
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• October 2020 - October 2021
• Institute of Human Genetics
• Web Developer
• Web application development and data architecture for various Liquid Biopsy projects at the institute. Digitalization and processing of the data for bioinformatical analysis.
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• April 2018 - May 2020
• Signal Processing and Speech Communication Lab
• Student assistant
• Collaboration with local company Atempo on the German language tool “CAPITO“. The work was presented at the IKT Forum at UNI Linz and served as a base for the currently publicly offered product.
• Collaboration with KnowCenter Graz on the drama digitalization project. The research was investigating methods of digitizing Shakespearean plays and how they further affect quantitative analysis of the play structure. A poster publication was presented as a result at Digital Humanities 2020 Ottawa.
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• Keldysh Institute of Applied Mathematics, Russia - Summer 2014 - Participation and assistance in various robotics projects at the institute. Robot programming, Arduino and Raspberry PI controllers, electrical equipment and sensors for intelligent robotics.
• LIEBHERR Russland, Russia - Summer-Winter 2016 - Web application developer intern. C# and PHP programming, database technologies and accounting system 1C.
Education
Graz University of Technology (Austria)
MSc, Computer Science
2017-2021
Russian State University for the Humanities (Russia)
BSc, Intelligent Systems in Humanities
2012-2016