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Do you MIND? Reflections on the MIND dataset for research on diversity in news recommendations

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arxiv 2304.08253 v2 pith:3VJQOS76 submitted 2023-04-17 cs.IR

classification cs.IR
keywords datasetmindnewsresearchdiversityhandrecommendationsanalysis
verification ladder T0 review T1 audit T2 compute T3 formal
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The MIND dataset is at the moment of writing the most extensive dataset available for the research and development of news recommender systems. This work analyzes the suitability of the dataset for research on diverse news recommendations. On the one hand we analyze the effect the different steps in the recommendation pipeline have on the distribution of article categories, and on the other hand we check whether the supplied data would be sufficient for more sophisticated diversity analysis. We conclude that while MIND is a great step forward, there is still a lot of room for improvement.

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  1. Leveraging Media Frames to Improve Normative Diversity in News Recommendations

    cs.IR 2025-09 conditional novelty 5.0 of 10

    Frame-based diversification in the MANNeR recommender increases predicted-frame novelty and measured normative diversity, but the gains are evaluated on the same auto-generated frame labels the system was optimized on.

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