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Fuzhen Zhuang
Researcher at Beihang University
Publications - 230
Citations - 8889
Fuzhen Zhuang is an academic researcher from Beihang University. The author has contributed to research in topics: Recommender system & Computer science. The author has an hindex of 35, co-authored 215 publications receiving 4113 citations. Previous affiliations of Fuzhen Zhuang include Capital Normal University & Soochow University (Suzhou).
Papers
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Journal ArticleDOI
A Comprehensive Survey on Transfer Learning
TL;DR: Transfer learning aims to improve the performance of target learners on target domains by transferring the knowledge contained in different but related source domains as discussed by the authors, in which the dependence on a large number of target-domain data can be reduced for constructing target learners.
Journal ArticleDOI
Deep Subdomain Adaptation Network for Image Classification
TL;DR: This work presents a deep subdomain adaptation network (DSAN) that learns a transfer network by aligning the relevant subdomain distributions of domain-specific layer activations across different domains based on a local maximum mean discrepancy (LMMD).
Proceedings ArticleDOI
Graph contextualized self-attention network for session-based recommendation
Chengfeng Xu,Pengpeng Zhao,Yanchi Liu,Victor S. Sheng,Jiajie Xu,Fuzhen Zhuang,Junhua Fang,Xiaofang Zhou +7 more
TL;DR: A graph contextualized self-attention model (GC-SAN) is proposed, which utilizes both graph neural network and self-Attention mechanism, for session-based recommendation and outperforms state-of-the-art methods consistently.
Proceedings Article
Supervised representation learning: transfer learning with deep autoencoders
TL;DR: This paper proposes a supervised representation learning method based on deep autoencoders for transfer learning that consists of an embedding layer and a label encoding layer that minimize the difference between domains explicitly and encode label information in learning the representation.
Proceedings ArticleDOI
Sequential recommender system based on hierarchical attention network
TL;DR: A novel two-layer hierarchical attention network is proposed, which takes the above properties into account, to recommend the next item user might be interested and demonstrates the superiority of the method compared with other state-of-the-art ones.