{"title":"你介意吗?关于MIND数据集对新闻推荐多样性研究的思考","authors":"Sanne Vrijenhoek","doi":"10.48550/arXiv.2304.08253","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":165601,"journal":{"name":"International Workshop on Algorithmic Bias in Search and Recommendation","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Do you MIND? Reflections on the MIND dataset for research on diversity in news recommendations\",\"authors\":\"Sanne Vrijenhoek\",\"doi\":\"10.48550/arXiv.2304.08253\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":165601,\"journal\":{\"name\":\"International Workshop on Algorithmic Bias in Search and Recommendation\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Workshop on Algorithmic Bias in Search and Recommendation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.48550/arXiv.2304.08253\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on Algorithmic Bias in Search and Recommendation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48550/arXiv.2304.08253","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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. 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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