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Low-Rank and Sparse Matrix Factorization for Scientific Paper Recommendation in Heterogeneous Network
- Source :
- IEEE Access, Vol 6, Pp 59015-59030 (2018)
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- © 2013 IEEE. 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 factorization-based paper recommendation (LSMFPRec) method for authors. The proposed method seamlessly combines low-rank and sparse matrix factorization method with fine-grained paper and author affinity matrixes that are extracted from heterogeneous scientific network. Thus, it can effectively alleviate the sparsity and cold start problems that exist in traditional matrix factorization based collaborative filtering methods. Moreover, LSMFPRec can significantly reduce the error propagated from intermediate outputs. In addition, the proposed method essentially captures the low-rank and sparse characteristics that exist in scientific rating activities; therefore, it can generate more reasonable predicted ratings for influential and uninfluential papers. The effectiveness of the proposed LSMFPRec is demonstrated by the recommendation evaluation conducted on the AAN and CiteULike data sets.
- Subjects :
- General Computer Science
Rank (linear algebra)
Computer science
heterogeneous network
General Engineering
02 engineering and technology
low rank and sparse matrix factorization
Recommender system
computer.software_genre
Matrix decomposition
Matrix (mathematics)
Paper recommendation
Cold start
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Collaborative filtering
020201 artificial intelligence & image processing
General Materials Science
Data mining
lcsh:Electrical engineering. Electronics. Nuclear engineering
computer
lcsh:TK1-9971
Heterogeneous network
Sparse matrix
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 6
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....3535e0ea06f81885fa33516ef6a5dd0b