Low-Rank and Sparse Matrix Factorization for Scientific Paper Recommendation in Heterogeneous Network

Low-Rank and Sparse Matrix Factorization for Scientific Paper Recommendation in Heterogeneous Network

With the rapid growth of scientific publications, it is hard for researchers to acquire appropriate papers that meet their expectations. Recommendation system for scientific articles is an essential technology to overcome this problem. In this paper, we propose a novel low-rank and sparse matrix fac...

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Bibliographic Details
Main Authors: Tao Dai, Tianyu Gao, Li Zhu, Xiaoyan Cai, Shirui Pan
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Paper recommendation
low rank and sparse matrix factorization
heterogeneous network
Online Access:https://ieeexplore.ieee.org/document/8434216/

Internet

https://ieeexplore.ieee.org/document/8434216/

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