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Recommenders with a mission: assessing diversity in newsrecommendations
News recommenders help users to find relevant online content and have the potential to fulfill a crucial role in a democratic society, directing the scarce attention of citizens towards the information that is most important to them.
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis
We collect a set of values that seem most relevant to recommender systems operating across different domains, then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches.
RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations
In traditional recommender system literature, diversity is often seen as the opposite of similarity, and typically defined as the distance between identified topics, categories or word models.
Do you MIND? Reflections on the MIND dataset for research on diversity in news recommendations
The MIND dataset is at the moment of writing the most extensive dataset available for the research and development of news recommender systems.
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.
Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content
In this position paper, we present a research agenda and ideas for facilitating exposure to diverse viewpoints in news recommendation.
