MIND: A Large-scale Dataset for News Recommendation

By Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu and 1 more. Annual Meeting of the Association for Computational Linguistics, 2020

670 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/dbdfd22ec8b71d48ff100819235eff56a4a374a3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Yong Li
4. Meng Wang
5. Chen Gao
6. Le Wu
7. Depeng Jin
8. Kun Zhang

In Computer Vision and Pattern Recognition

1. Xiangnan He
2. Xiang Wang
3. Yong Li
4. Chen Gao
5. Meng Wang
6. Le Wu
7. Depeng Jin
8. Kun Zhang

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Yong Li
5. Chen Gao
6. Le Wu
7. Kun Zhang
8. Depeng Jin

Citations in the corpus

• DA-Transformer: Distance-aware Transformer (2020, background): “…architecture of various state-of-the-art models like BERT (Devlin et al., 2019) and GPT (Radford et al., 2019), and boosts the performance of many tasks like text generation (Koncel-Kedziorski et al., 2019), machine translation (Vaswani et al., 2017), and reading comprehension (Xu et al., 2019).”
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, background): “As for news recommendation, MIND dataset [192] is the recently-released representative benchmark dataset.”
• Is ChatGPT a Good Recommender? A Preliminary Study (2023, background): “…recommendation systems have witnessed significant advancements over the past decade and have been widely used in various web applications such as product recommendation [32, 50, 52, 60], video recommendation [40, 55, 67], news recommendation [56–58], music recommendation [27, 48] and so on.”
• MTEB: Massive Text Embedding Benchmark (2022, methodology): “MindSmall (Wu et al., 2020) Large-scale English Dataset for News Recommendation Research.”
• Towards General Text Embeddings with Multi-stage Contrastive Learning (2023, positive): “Microsoft News data comes from Wu et al. (2020).”
• NewsEmbed: Modeling News through Pre-trained Document Representations (2021, methodology): “We consider four popularly used datasets that cover a variety of publishers and news styles: BBC [24], 20 Newsgroups8, AG News [63], and MIND-small [59], with statistics outlined in Table 7.”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “In order to advance the recommendation community, some researchers make efforts on the data level, such as industry-relevant recommendation benchmark [275], MIcrosoft News Dataset (MIND) [276], and Yelp dataset 2 .”
• Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited (2023, methodology): “text [70], images [42], videos [9], audio [61] and text-image multimodal pairs [68].”
• Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMs (2026, unknown): “…Retrieval nDCG@10 Touche2020 Retrieval nDCG@10 TRECCOVID [57] Retrieval nDCG@10 Other (6) AskUbuntuDupQuestions Reranking MAP MindSmallReranking [74] Reranking MAP SICK-R [42] STS Spearman STSBenchmark [7] STS Spearman SprintDuplicateQuestions [59] Duplicate detection AP TwitterURLCorpus [34]…”
• Length-Induced Embedding Collapse in PLM-based Models (2024, unknown): “MindSmall (Wu et al., 2020) dataset is a comprehensive English resource designed for research in news recommendation, focusing on ranking news articles based on the title of a currently read article to suggest related content.”