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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/219965
Characterization and Mitigation of Algorithmic Bias in Recommender Systems
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[eng] Recommender Systems are critical in helping users navigate large amounts of information by providing personalized suggestions. However, these systems can exhibit biases, especially when data imbalances exist, leading to unfair recommendations that favor more popular or majority items over those from minority groups. This thesis explores the identification, characterization, and mitigation of algorithmic bias within Recommender Systems. This research focuses on addressing biases that arise from data imbalances and how these biases can lead to unfair treatment of certain groups, particularly in terms of visibility and exposure in recommendations. The primary goal of the thesis is to mitigate algorithmic bias in Recommender Systems to produce fairer and more equitable recommendation lists, through techniques of post-processing bias mitigation (e.g., re-ranking recommendation results to ensure fairness). This includes identifying and categorizing biases in datasets, designing strategies to mitigate these biases, and developing techniques to optimize recommendation algorithms to reduce bias.
The main contributions of this thesis are five, divided into two thematic parts. The first thematic part focuses on Provider Fairness and the second thematic part on Fairness from Multiple Perspectives.
Regarding the first thematic part, two contributions have been made. In the first, a Binary Approach was adopted, by categorizing geographic bias or imbalance associated with the country of production of the items and identifying two groups of providers (majority versus rest), and based on the distribution observed in the original training set, the recommendations are adjusted to align with these groups, with the aim of mitigating disparity bias. In the second contribution, we explain the process of categorization and bias mitigation using a Multi-Class Approach. We explore how recommendation algorithms can exacerbate biases by promoting items from certain regions, which could disadvantage underrepresented geographic groups.
Concerning the second thematic part, three contributions have been made. The first contribution introduces CONFIGRE, a novel methodology designed to ensure fairness in Recommender Systems by balancing visibility between coarse- and fine-grained demographic groups. In second contribution we present MOReGln, a new approach for managing multiple objectives in Recommender Systems. This method specifically addresses the challenge of achieving both global balance and individual fairness in recommendations. Finally, in an additional contribution, we develop a new dataset (AMBAR, in the music domain) that includes sensitive attributes
at various levels of granularity. Furthermore, we extend two real-world datasets (MovieLenslM and Book-Crossing) with geographic information to study the link between geographic imbalance and disparate impact.
This thesis advances on the identification, characterization, mitigation and evaluation of biases in collaborative Recommender Systems. It addresses existing gaps in the analysis of geographical biases in different group settings: from binary groups, multi-class groups to different levels of granularity of groups. The outlined contributions establish a basis for further advancements and effective mitigation of biases without significantly compromising accuracy. Our findings, developed software, and resources presented in this dissertation are available to the community to facilitate further research and knowledge transfer.
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GÓMEZ YEPES, Elizabeth. Characterization and Mitigation of Algorithmic Bias in Recommender Systems. [consulta: 30 de novembre de 2025]. [Disponible a: https://hdl.handle.net/2445/219965]