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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8434216/ |
| _version_ | 1819170145357004800 |
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| author | Tao Dai Tianyu Gao Li Zhu Xiaoyan Cai Shirui Pan |
| author_facet | Tao Dai Tianyu Gao Li Zhu Xiaoyan Cai Shirui Pan |
| author_sort | Tao Dai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T19:30:44Z |
| format | Article |
| id | doaj.art-980c84eeac624374ad7aad06ec5e72f4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:30:44Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-980c84eeac624374ad7aad06ec5e72f42022-12-21T18:15:07ZengIEEEIEEE Access2169-35362018-01-016590155903010.1109/ACCESS.2018.28651158434216Low-Rank and Sparse Matrix Factorization for Scientific Paper Recommendation in Heterogeneous NetworkTao Dai0Tianyu Gao1Li Zhu2https://orcid.org/0000-0003-2136-3196Xiaoyan Cai3https://orcid.org/0000-0002-1406-107XShirui Pan4https://orcid.org/0000-0003-0794-527XSchool of Software Engineering, Xi’an Jiaotong University, Xi’an, ChinaSchool of Software Engineering, Xi’an Jiaotong University, Xi’an, ChinaSchool of Software Engineering, Xi’an Jiaotong University, Xi’an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an, ChinaCentre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, AustraliaWith 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.https://ieeexplore.ieee.org/document/8434216/Paper recommendationlow rank and sparse matrix factorizationheterogeneous network |
| spellingShingle | Tao Dai Tianyu Gao Li Zhu Xiaoyan Cai Shirui Pan Low-Rank and Sparse Matrix Factorization for Scientific Paper Recommendation in Heterogeneous Network IEEE Access Paper recommendation low rank and sparse matrix factorization heterogeneous network |
| title | Low-Rank and Sparse Matrix Factorization for Scientific Paper Recommendation in Heterogeneous Network |
| title_full | Low-Rank and Sparse Matrix Factorization for Scientific Paper Recommendation in Heterogeneous Network |
| title_fullStr | Low-Rank and Sparse Matrix Factorization for Scientific Paper Recommendation in Heterogeneous Network |
| title_full_unstemmed | Low-Rank and Sparse Matrix Factorization for Scientific Paper Recommendation in Heterogeneous Network |
| title_short | Low-Rank and Sparse Matrix Factorization for Scientific Paper Recommendation in Heterogeneous Network |
| title_sort | low rank and sparse matrix factorization for scientific paper recommendation in heterogeneous network |
| topic | Paper recommendation low rank and sparse matrix factorization heterogeneous network |
| url | https://ieeexplore.ieee.org/document/8434216/ |
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