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DTSTART;TZID=Europe/Oslo:20251215T091500
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DTSTAMP:20260404T141617
CREATED:20251006T085055Z
LAST-MODIFIED:20251006T085055Z
UID:21692-1765790100-1765807200@mediafutures.no
SUMMARY:Popularity Bias in Recommender Systems​
DESCRIPTION:We are pleased to invite you to the trial lecture and public defence of Anastasiia Klimashevskaia\, PhD candidate at SFI MediaFutures. \n Date: 15 December Venue: Ulrikes Aula\, University of Bergen \nTime: To be confirmed \nPhD Thesis:Beyond Popularity: Investigating and Mitigating Bias in Recommender Systems \nThesis Summary:Recommender systems are powerful tools shaping what users see and engage with online. However\, they often suffer from popularity bias\, where already popular items are disproportionately promoted while niche content remains underrepresented. This bias reduces diversity\, user satisfaction\, and fairness across platforms. \nIn her doctoral work\, Anastasiia Klimashevskaia examines the causes and effects of popularity bias through a comprehensive literature review\, explores debiasing strategies using real-world datasets\, and evaluates their performance in an online A/B test within a live recommender system. \nHer research further investigates how popularity bias interacts with other algorithmic biases and proposes novel mitigation strategies based on alternative theoretical frameworks. The findings shed light on the trade-offs between fairness\, diversity\, and recommendation quality—contributing to the creation of more equitable recommender systems. \nAll are warmly welcome to attend and take part in celebrating Anastasiia’s important milestone.
URL:https://mediafutures.no/event/popularity-bias-in-recommender-systems/
LOCATION:Ulrike Pihls Hus\, Ulrikes aula\, Professor Keysers gate 1\, Bergen\, Norway
CATEGORIES:Events
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