{
 "id": "mteb/MindSmallReranking",
 "owner": "mteb",
 "name": "MindSmallReranking",
 "downloads": 3002,
 "likes": 3,
 "last_modified": "2025-10-19T14:11:24.000Z",
 "stated_licence": "other",
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 "licence_name": null,
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 "sources": [],
 "gated": false,
 "tasks": [
  "text-ranking"
 ],
 "citation": "@inproceedings{wu-etal-2020-mind,\n  abstract = {News recommendation is an important technique for personalized news\nservice. Compared with product and movie recommendations which have been comprehensively studied,\nthe research on news recommendation is much more limited, mainly due to the lack of a high-quality benchmark\ndataset. In this paper, we present a large-scale dataset named MIND for news recommendation. Constructed from\nthe user click logs of Microsoft News, MIND contains 1 million users and more than 160k English news\narticles, each of which has rich textual content such as title, abstract and body. We demonstrate MIND a good\ntestbed for news recommendation through a comparative study of several state-of-the-art news recommendation\nmethods which are originally developed on different proprietary datasets. Our results show the performance of\nnews recommendation highly relies on the quality of news content understanding and user interest modeling.\nMany natural language processing techniques such as effective text representation methods and pre-trained\nlanguage models can effectively improve the performance of news recommendation. The MIND dataset will be\navailable at https://msnews.github.io.},\n  address = {Online},\n  author = {Wu, Fangzhao  and Qiao, Ying  and Chen, Jiun-Hung  and Wu, Chuhan  and Qi,\nTao  and Lian, Jianxun  and Liu, Danyang  and Xie, Xing  and Gao, Jianfeng  and Wu, Winnie  and Zhou, Ming},\n  booktitle = {Proceedings of the 58th Annual Meeting of the ",
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 "revision": "564417522344e08a0cc5fed877fdbc74c51a5899",
 "stated_evidence": "https://huggingface.co/datasets/mteb/MindSmallReranking/blob/564417522344e08a0cc5fed877fdbc74c51a5899/README.md",
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