# name
Analysis of Content Diversity in News Recommendation Systems

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Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Any: 2026. Tutor: Oriol Pujol i Jordi Vitrià

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This Master’s Thesis studies informational diversity in news recommendation systems from a dual perspective: producer diversity and consumer-perceived diversity. The main objective is to analyze which diversity metrics are most suitable for evaluating journalistic content and how they can be applied to different datasets. In a first phase, a study is conducted on various diversity metrics used in recommendation systems, taking as reference those proposed in the literature associated with Microsoft and using the MIND Small dataset. This analysis allows the evaluation of classical diversity and coverage metrics applied to news recommendation, as well as an understanding of their advantages and limitations in controlled environments. In a second phase, these metrics are adapted and applied to a news dataset from the media outlet 3Cat, aiming to evaluate the diversity of political topics present in the published content. In this context, a distinction is made between producer diversity, related to the ideological and thematic variety of the generated content, and consumer diversity, modeled through an activation metric that approximates the diversity effectively perceived by the reader. The results allow for a comparison of both diversity perspectives and the analysis of potential mismatches between the informational supply and the diversity experienced by the user. The code developed for data processing and metric calculation can be found in the project repository (Blanco Borrás, 2026).

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memòria: cc-by-nc-nd (c) Rubén Blanco Borrás, 2026

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codi: Apache (c) Rubén Blanco Borrás, 2026

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Màster Oficial - Fonaments de la Ciència de Dades

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Tractament del llenguatge natural (Informàtica)

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Natural language processing (Computer science)

# metadata.dc.title[].value
Analysis of Content Diversity in News Recommendation Systems