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Fangzhao Wu
Researcher at Microsoft
Publications - 119
Citations - 3111
Fangzhao Wu is an academic researcher from Microsoft. The author has contributed to research in topics: Computer science & User modeling. The author has an hindex of 22, co-authored 117 publications receiving 1419 citations. Previous affiliations of Fangzhao Wu include Tsinghua University.
Papers
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Proceedings ArticleDOI
MIND: A Large-scale Dataset for News Recommendation
Fangzhao Wu,Ying Qiao,Jiun-Hung Chen,Chuhan Wu,Tao Qi,Jianxun Lian,Danyang Liu,Xing Xie,Jianfeng Gao,Winnie Wu,Ming Zhou +10 more
TL;DR: This paper presents a large-scale dataset named MIND, constructed from the user click logs of Microsoft News, which contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body.
Proceedings ArticleDOI
NPA: Neural News Recommendation with Personalized Attention
TL;DR: In this article, a neural news recommendation model with personalized attention (NPA) is proposed, which exploits the embedding of user ID to generate the query vector for the word-and news-level attentions.
Proceedings ArticleDOI
Neural News Recommendation with Long- and Short-term User Representations
TL;DR: A neural news recommendation approach which can learn both long- and short-term user representations and which can effectively improve the performance of neuralNews recommendation.
Proceedings ArticleDOI
Neural News Recommendation with Multi-Head Self-Attention.
TL;DR: A neural news recommendation approach with multi-head self-attentions to learn news representations from news titles by modeling the interactions between words and applies additive attention to learn more informative news and user representations by selecting important words and news.
Posted Content
Neural News Recommendation with Attentive Multi-View Learning
TL;DR: A neural news recommendation approach which can learn informative representations of users and news by exploiting different kinds of news information and can effectively improve the performance of news recommendation is proposed.