Anastasiia Klimashevskaia
PhD Candidate, MediaFutures: Research Centre for Responsible Media Technology & Innovation
- E-mailanastasiia.klimashevskaia@uib.no
- Visitor AddressLars Hilles gate 305008 Bergen
- Postal AddressPostboks 78025020 Bergen
Research in the field of Recommender Systems, Natural Language Processing and Digital Humanities.
Previously worked on:
- Language modeling, tokenization, homonymy resolution
- Textual fact extraction
- Natural language generation
- Literature digitalization and analysis
Currently working on:
- Bias and fairness issues in recommender systems
- Popularity bias mitigation strategies for recommender systems
Lecture
- (2023). Undesired Effects and Popularity Bias In Recommendation.
- (2023). Recommender Systems: Benefits and Pitfalls of Personalization.
- (2023). Recommender Systems: Benefits and Pitfalls of Personalization.
- (2023). Recommender Systems, Natural Language Processing and Digital Humanities.
- (2023). Recommender Systems Beyond Accuracy: Biases and Unfairness in Personalization.
- (2023). Recommender Systems Beyond Accuracy: Biases and Unfairness in Personalization.
- (2023). Addressing Popularity Bias in Recommender Systems: An Exploration of Self-Supervised Learning Models.
- (2022). Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches.
Academic lecture
- (2022). Popularity Bias as Ethical and Technical Issue in Recommendation: A Survey.
Academic anthology/Conference proceedings
- (2023). Addressing Popularity Bias in Recommender Systems: An Exploration of Self-Supervised Learning Models. Association for Computing Machinery (ACM).
Academic chapter/article/Conference paper
- (2023). Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study. 6 pages.
- (2022). Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches. 9 pages.
Poster
- (2023). Addressing Popularity Bias in Recommender Systems: An Exploration of Self-Supervised Learning Models.
- (2021). Exploring Recommender Systems: Towards Fair and Ethical Recommendation.
Academic literature review
- (2022). Popularity Bias as Ethical and Technical Issue in Recommendation: A Survey. Norsk Informatikkonferanse (NIK).
More information in national current research information system (CRIStin)
- I am fluent in Russian and English, speak good German with an Austrian dialect and know a bit of Italian and Norwegian.
- I've attended kids art school for 7 years and wanted to become an artist before I ended up in Computer Science.
- I like hiking, making pictures, reading, cooking and playing games - both computer and tabletop.
- I've lived most of my life in Moscow, however have also spent considerable amount of time in Graz, Austria, where I did my Master study. I've also had an opportunity to stay in San Luis Obispo, California, US for 7 months during my master thesis research. Now I am residing in Bergen, Norway.
- I am a big cat person ~(=^‥^)/
Fields of competence