J
Jianxun Lian
Researcher at Microsoft
Publications - 32
Citations - 2586
Jianxun Lian is an academic researcher from Microsoft. The author has contributed to research in topics: Recommender system & Collaborative filtering. The author has an hindex of 13, co-authored 32 publications receiving 1092 citations. Previous affiliations of Jianxun Lian include University of Science and Technology of China.
Papers
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Proceedings ArticleDOI
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
TL;DR: A novel Compressed Interaction Network (CIN), which aims to generate feature interactions in an explicit fashion and at the vector-wise level and is named eXtreme Deep Factorization Machine (xDeepFM), which is able to learn certain bounded-degree feature interactions explicitly and can learn arbitrary low- and high-order feature interactions implicitly.
Proceedings ArticleDOI
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
TL;DR: Wang et al. as mentioned in this paper proposed a Compressed Interaction Network (CIN), which aims to generate feature interactions in an explicit fashion and at the vector-wise level.
Proceedings ArticleDOI
Self-supervised Graph Learning for Recommendation
TL;DR: This work explores self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation, and implements it on the state-of-the-art model LightGCN, which has the ability of automatically mining hard negatives.
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
Self-supervised Graph Learning for Recommendation
TL;DR: Wu et al. as discussed by the authors explored self-supervised learning on user-item graph, so as to improve the accuracy and robustness of graph convolutional networks for recommendation.