MIND: A Large-scale Dataset for News Recommendation

Wu, Fangzhao; Qiao, Ying; Chen, Jiun-Hung; Wu, Chuhan; Qi, Tao; Lian, Jianxun; Liu, Danyang; Xie, Xing; Gao, Jianfeng; Wu, Winnie; Zhou, Ming

doi:10.18653/v1/2020.acl-main.331

Cited by 320 publications

(216 citation statements)

References 33 publications

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“…The MIND dataset is a large-scale benchmark dataset (Wu et al 2020b) for news recommendation research. MIND contains about 160 k English news articles, and more than 15 million impression logs generated by 1 million users.…”

Section: Microsoft News Dataset (Mind)mentioning

confidence: 99%

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News recommender system: a review of recent progress, challenges, and opportunities

2021

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Nowadays, more and more news readers read news online where they have access to millions of news articles from multiple sources. In order to help users find the right and relevant content, news recommender systems (NRS) are developed to relieve the information overload problem and suggest news items that might be of interest for the news readers. In this paper, we highlight the major challenges faced by the NRS and identify the possible solutions from the state-of-the-art. Our discussion is divided into two parts. In the first part, we present an overview of the recommendation solutions, datasets, evaluation criteria beyond accuracy and recommendation platforms being used in the NRS. We also talk about two popular classes of models that have been successfully used in recent years. In the second part, we focus on the deep neural networks as solutions to build the NRS. Different from previous surveys, we study the effects of news recommendations on user behaviors and try to suggest possible remedies to mitigate those effects. By providing the state-ofthe-art knowledge, this survey can help researchers and professional practitioners have a better understanding of the recent developments in news recommendation algorithms. In addition, this survey sheds light on the potential new directions.

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Section: Microsoft News Dataset (Mind)mentioning

confidence: 99%

“…MIND (Wu et al 2020b) is a recent news benchmark dataset. The contributors of the dataset provided an environment in the form of a competitive event and Leaderboard 23 for researchers to work on the news recommendation problem.…”

Section: Open News Recommendation Platformsmentioning

confidence: 99%

News recommender system: a review of recent progress, challenges, and opportunities

2021

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“…By making these visualizations as intuitive as possible, they should facilitate the discussion between data science teams, editors and upper management around this topic. To make this approach reusable and broadly applicable, it should be implemented and tested on both a benchmark set such as [55] and in a real-life setting. We are in contact with multiple media companies, to inform them about the different models of democracy, facilitate the discussion around this subject, and stimulate and test the implementation of our tool.…”

Section: Methodsmentioning

confidence: 99%

Recommenders with a Mission

Vrijenhoek

Kaya²,

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et al. 2021

Proceedings of the 2021 Conference on Human Information Interaction and Retrieval

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News recommenders help users to find relevant online content and have the potential to fulfill a crucial role in a democratic society, directing the scarce attention of citizens towards the information that is most important to them. Simultaneously, recent concerns about so-called filter bubbles, misinformation and selective exposure are symptomatic of the disruptive potential of these digital news recommenders. Recommender systems can make or break filter bubbles, and as such can be instrumental in creating either a more closed or a more open internet. Current approaches to evaluating recommender systems are often focused on measuring an increase in user clicks and short-term engagement, rather than measuring the user's longer term interest in diverse and important information.This paper aims to bridge the gap between normative notions of diversity, rooted in democratic theory, and quantitative metrics necessary for evaluating the recommender system. We propose a set of metrics grounded in social science interpretations of diversity and suggest ways for practical implementations.

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“…The detailed statistics of these datasets are summarized in Table 1. In addition, we also conduct experiments on a benchmark news recommendation dataset named MIND (Wu et al, 2020c), aiming to validate the effectiveness of our approach in both text and user modeling. It contains the news impression logs of 1 million users from Microsoft News 3 from October 12 to November 22, 2019.…”

Section: Datasets and Experimental Settingsmentioning

confidence: 99%

DA-Transformer: Distance-aware Transformer

Wu¹,

Wu²,

Huang³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Transformer has achieved great success in the NLP field by composing various advanced models like BERT and GPT. However, Transformer and its existing variants may not be optimal in capturing token distances because the position or distance embeddings used by these methods usually cannot keep the precise information of real distances, which may not be beneficial for modeling the orders and relations of contexts. In this paper, we propose DA-Transformer, which is a distanceaware Transformer that can exploit the real distance. We propose to incorporate the real distances between tokens to re-scale the raw self-attention weights, which are computed by the relevance between attention query and key. Concretely, in different self-attention heads the relative distance between each pair of tokens is weighted by different learnable parameters, which control the different preferences on long-or short-term information of these heads. Since the raw weighted real distances may not be optimal for adjusting selfattention weights, we propose a learnable sigmoid function to map them into re-scaled coefficients that have proper ranges. We first clip the raw self-attention weights via the ReLU function to keep non-negativity and introduce sparsity, and then multiply them with the rescaled coefficients to encode real distance information into self-attention. Extensive experiments on five benchmark datasets show that DA-Transformer can effectively improve the performance of many tasks and outperform the vanilla Transformer and its several variants.

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MIND: A Large-scale Dataset for News Recommendation

Cited by 320 publications

References 33 publications

News recommender system: a review of recent progress, challenges, and opportunities

News recommender system: a review of recent progress, challenges, and opportunities

Recommenders with a Mission

DA-Transformer: Distance-aware Transformer

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