MIND : A Large-scale Dataset for News Recommendation (2020)

Abstract: News recommendation is an important technique for personalized news service. Compared with product and movie recommendations which have been comprehensively studied, the research on news recommendation is much more limited, mainly due to the lack of a high-quality benchmark dataset. In this paper, we present a large-scale dataset named MIND for news recommendation. Constructed from the user click logs of Microsoft News, MIND contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body. We demonstrate MIND a good testbed for news recommendation through a comparative study of several state-of-the-art news recommendation methods which are originally developed on different proprietary datasets. Our results show the performance of news recommendation highly relies on the quality of news content understanding and user interest modeling. Many natural language processing techniques such as effective text representation methods and pre-trained language models can effectively improve the performance of news recommendation. The MIND dataset will be available at https://msnews.github.io .

## introduction online news services such as google news and microsoft news have become important platforms for a large population of users to obtain news information @xcite . massive news articles are generated and posted online every day, making it difficult for users to find interested news quickly @xcite . personalized news recommendation can help users alleviate information overload and improve news reading experience @xcite . thus, it is widely used in many online news platforms @xcite @xcite . in traditional recommender systems, users and items are usually represented using ids, and their interactions such as rating scores are used to learn id representations via methods like collaborative filtering @xcite . however, news recommendation has some special challenges. first, news articles on news websites update very quickly. new news articles are posted continuously, and existing news articles will expire in short time @xcite . thus, the cold-start problem is very severe in news recommendation. second, news articles contain rich textual information such as title and body. it is not appropriate to simply representing them using ids, and it is important to understand their content from their texts @xcite . third, there is no explicit rating of news articles posted by users on news platforms. thus, in news recommendation users' interest in news is usually inferred from their click behaviors in an implicit way @xcite . a large-scale and high-quality dataset can significantly facilitate the research in an area, such as imagenet for image classification @xcite and squad for machine reading comprehension @xcite . there are several public datasets for traditional recommendation tasks, such as amazon dataset foot_0 for product recommendation and movielens dataset foot_1 for movie recommendation. based on these datasets, many well-known recommendation methods have been developed. however, existing studies on news recommendation are much fewer, and many of them are conducted on proprietary datasets @xcite @xcite . although there are a few public datasets for news recommendation, they are usually in small size and most of them are not in english. thus, a public title mike tomlin: steelers 'accept responsibility' for role in brawl with browns category sports abstract mike tomlin has admitted that the pittsburgh steelers played a role in the brawl with the cleveland browns last week, and on tuesday he accepted responsibility for it on behalf of the organization. body tomlin opened his weekly news conference by addressing the issue head on. "it was ugly," said tomlin, who had refused to take any questions about the incident directly after the game, per brooke pryor of espn. "it was ugly for the game of football. i think all of us that are involved in the game, particularly at this level, … large-scale english news recommendation dataset is of great value for the research in this area. in this paper we present a large-scale microsoft news dataset (mind) for news recommendation research, which is collected from the user behavior logs of microsoft news foot_2 . it contains 1 million users and their click behaviors on more than 160k english news articles. we implement many state-ofthe-art news recommendation methods originally developed on different proprietary datasets, and compare their performance on the mind dataset to provide a benchmark for news recommendation research. the experimental results show that a deep understanding of news articles through nlp techniques is very important for news recommendation. both effective text representation methods and pre-trained language models can contribute to the performance improvement of news recommendation. in addition, appropriate modeling of user interest is also useful. we hope mind can serve as a benchmark dataset for news recommendation and facilitate the research in this area. ## method in this section, we briefly introduce several methods for news recommendation, including general recommendation methods and news-specific recommendation methods. these methods were developed in different settings and on different datasets. some of their implementations can be found in microsoft recommenders open source repository foot_10 . we will compare them on the mind dataset. ## conclusion and discussion in this paper we present the mind dataset for news recommendation research, which is constructed from user behavior logs of microsoft news. it contains 1 million users and more than 160k english news articles with rich textual content such as title, abstract and body. we conduct extensive experiments on this dataset. the results show the importance of accurate news content understanding and user interest modeling for news recommendation. many natural language processing and machine learning techniques such as text modeling, attention mechanism and pre-trained language models can contribute to the performance improvement of news recommendation. in the future, we plan to extend the mind dataset by incorporating image and video information in news as well as news in different languages, which can support the research of multi-modal and multi-lingual news recommendation. in addition, besides the click behaviors, we plan to incorporate other user behaviors such as read and engagement to support more accurate user modeling and performance evaluation. many interesting researches can be conducted on the mind dataset, such as designing better news and user modeling methods, improving the diversity, fairness and explainability of news recommendation results, and exploring privacy-preserving news recommendation. besides news recommendation, the mind dataset can also be used in other natural language processing tasks such as topic classification, text summarization and news headline generation.