MIcrosoft News Dataset (MIND) is a large-scale dataset for news recommendation research. It was collected from anonymized behavior logs of Microsoft News website. The mission of MIND is to serve as a benchmark dataset for news recommendation and facilitate the research in news recommendation and recommender systems area.
MIND contains about 160k English news articles and more than 15 million impression logs generated by 1 million users. Every news article contains rich textual content including title, abstract, body, category and entities. Each impression log contains the click events, non-clicked events and historical news click behaviors of this user before this impression. To protect user privacy, each user was de-linked from the production system when securely hashed into an anonymized ID. For more detailed information about the MIND dataset, you can refer to the following paper:
If you are interested in using this dataset in your research work, welcome to cite this paper:
Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu and Ming Zhou. MIND: A Large-scale Dataset for News Recommendation. ACL 2020.
The MIND dataset is free to download for research purposes under Microsoft Research License Terms. Before you download the dataset, please read these terms and click below button to confirm that you agree to them.
This dataset can support many researches on news recommendation, and can be downloaded at:
We also provide the link to Microsoft Azure Open Datasets for eaiser access to our large dataset on cloud platform:
For more details about the data formats, please refer to this document:
In addition, to help the researchers get familiar with our data and run quick experiments, we release a small version of the MIND dataset by randomly sampling 50,000 users and their behavior logs from the MIND dataset. We name this dataset MIND-small. The training and validation sets of MIND-small can be downloaded at:
The implementation of several existing news recommendation methods and general recommendation methods can be found at Microsoft Recommenders.