H
Huiyuan Chen
Researcher at Case Western Reserve University
Publications - 14
Citations - 278
Huiyuan Chen is an academic researcher from Case Western Reserve University. The author has contributed to research in topics: Recommender system & Drug repositioning. The author has an hindex of 7, co-authored 14 publications receiving 148 citations.
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Journal ArticleDOI
iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding.
TL;DR: This paper presents a novel approach called iDrug, which seamlessly integrates drug repositioning and drug-target prediction into one coherent model via cross-network embedding and provides a principled way to transfer knowledge from these two domains and to enhance prediction performance for both tasks.
Proceedings ArticleDOI
Adversarial tensor factorization for context-aware recommendation
Huiyuan Chen,Jing Li +1 more
TL;DR: Tensor factorization and adversarial learning are combined for context-aware recommendations to reap the benefits of tensor factorization, while enhancing the robustness of a recommender model, and thus improves its eventual performance.
Proceedings ArticleDOI
Learning Multiple Similarities of Users and Items in Recommender Systems
Huiyuan Chen,Jing Li +1 more
TL;DR: The key idea of MSUI is to simultaneously capture the associations between latent factors in the U-I matrix and multiple side sources of users and items through joint nonnegative matrix factorization.
Proceedings ArticleDOI
Exploiting Structural and Temporal Evolution in Dynamic Link Prediction
Huiyuan Chen,Jing Li +1 more
TL;DR: A novel framework named STEP is proposed, to simultaneously integrate both structural and temporal information in link prediction in dynamic networks, and can be used to solve the link prediction problem in directed or undirected, weighted or unweighted dynamic networks.
Proceedings ArticleDOI
Modeling Relational Drug-Target-Disease Interactions via Tensor Factorization with Multiple Web Sources
Huiyuan Chen,Jing Li +1 more
TL;DR: This work investigates the utility of tensor factorization to model the relationships of drug-target-disease, specifically leveraging different types of online data and elegantly explores a tensor together with complementarity among different data sources, thus improves prediction accuracy.