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

Poster

• Klimashevskaia, Anastasiia (2024). Philosophical Perspectives on Recommender Systems Ethical Implications, Biases, and Their Impact on Autonomy and Privacy. (external link)
• Klimashevskaia, Anastasiia (2023). Addressing Popularity Bias in Recommender Systems: An Exploration of Self-Supervised Learning Models. (external link)
• Klimashevskaia, Anastasiia (2021). Exploring Recommender Systems: Towards Fair and Ethical Recommendation. (external link)

Lecture

• Klimashevskaia, Anastasiia (2024). News in AI age: Alignment with Editorial Values and Ethics. (external link)
• Klimashevskaia, Anastasiia (2023). Recommender Systems: Benefits and Pitfalls of Personalization. (external link)
• Klimashevskaia, Anastasiia (2023). Recommender Systems, Natural Language Processing and Digital Humanities. (external link)
• Klimashevskaia, Anastasiia (2023). Addressing Popularity Bias in Recommender Systems: An Exploration of Self-Supervised Learning Models. (external link)
• Klimashevskaia, Anastasiia (2023). Recommender Systems Beyond Accuracy: Biases and Unfairness in Personalization. (external link)
• Klimashevskaia, Anastasiia (2023). Undesired Effects and Popularity Bias In Recommendation. (external link)
• Klimashevskaia, Anastasiia (2023). Recommender Systems: Benefits and Pitfalls of Personalization. (external link)
• Klimashevskaia, Anastasiia (2023). Recommender Systems Beyond Accuracy: Biases and Unfairness in Personalization. (external link)
• Klimashevskaia, Anastasiia (2022). Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches. (external link)

Academic literature review

• Klimashevskaia, Anastasiia; Jannach, Dietmar; Elahi, Mehdi et al. (2024). A survey on popularity bias in recommender systems. (external link)
• Klimashevskaia, Anastasiia (2022). Popularity Bias as Ethical and Technical Issue in Recommendation: A Survey. (external link)

Academic anthology/Conference proceedings

• Klimashevskaia, Anastasiia; Elahi, Mehdi; Trattner, Christoph (2023). Addressing Popularity Bias in Recommender Systems: An Exploration of Self-Supervised Learning Models. (external link)

Academic chapter/article/Conference paper

• Klimashevskaia, Anastasiia; Elahi, Mehdi; Jannach, Dietmar et al. (2023). Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study. (external link)
• Klimashevskaia, Anastasiia; Elahi, Mehdi; Jannach, Dietmar et al. (2022). Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches.. (external link)

Academic lecture

• Klimashevskaia, Anastasiia (2022). Popularity Bias as Ethical and Technical Issue in Recommendation: A Survey. (external link)

See a complete overview of publications in Cristin.

• Investigating Bias Mitigation Techniques for Fairness and Diversity in Recommender Systems - PhD Research at MediaFutures in collaboration with TV2 Norway