48 Papers
58 Citations
Linmei Hu is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 10, co-authored 36 publications. Previous affiliations of Linmei Hu include Tsinghua University.
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Papers
Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
Shaohua Fan,Junxiong Zhu,Xiaotian Han,Chuan Shi,Linmei Hu,Biyu Ma,Li Yongliang +6 more
- 25 Jul 2019
TL;DR: A metapath-guided heterogeneous Graph Neural Network to learn the embeddings of objects in intent recommendation as a Heterogeneous Information Network is proposed and Offline experiments on real large-scale data show the superior performance of the proposed MEIRec, compared to representative methods.
386
HGAT: Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification
TL;DR: A novel heterogeneous graph neural network-based method for semi-supervised short text classification, leveraging full advantage of limited labeled data and large unlabeled data through information propagation along the graph.
Graph neural news recommendation with long-term and short-term interest modeling
TL;DR: This paper proposes to build a heterogeneous graph to explicitly model the interactions among users, news and latent topics and shows that the proposed model significantly outperforms state-of-the-art methods on news recommendation.
Graph Neural News Recommendation with Unsupervised Preference Disentanglement
Linmei Hu,Siyong Xu,Chen Li,Cheng Yang,Chuan Shi,Nan Duan,Xing Xie,Ming Zhou +7 more
- 01 Jul 2020
TL;DR: This paper model the user-news interactions as a bipartite graph and proposes a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement, named GNUD, which can effectively improve the performance of news recommendation and outperform state-of-the-art news recommendation methods.
152
Relation Structure-Aware Heterogeneous Information Network Embedding
Yuanfu Lu,Chuan Shi,Linmei Hu,Zhiyuan Liu +3 more
- 17 Jul 2019
TL;DR: This paper takes the structural characteristics of heterogeneous relations into consideration and proposes a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE), which significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification.