A scientific citation recommendation model integrating network and text representations.
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| Title: | A scientific citation recommendation model integrating network and text representations. |
|---|---|
| Authors: | Qiu, Tianshuang1 (AUTHOR), Yu, Chuanming1 (AUTHOR) yucm@zuel.edu.cn, Zhong, Yunci1 (AUTHOR), An, Lu2 (AUTHOR), Li, Gang2 (AUTHOR) |
| Source: | Scientometrics. Nov2021, Vol. 126 Issue 11, p9199-9221. 23p. |
| Subjects: | Logistic regression analysis, Acquisition of data, Information networks, Citation networks |
| Abstract: | The number of scientific papers is increasing in the rapid growth. How to make paper acquisition efficient and provide effective citation recommendation is essential for researchers. Although the application of scientific citation recommendation has shown great improvements, the in-depth mining and fusion of various types of information has been ignored. In this paper, we propose a scientific citation recommendation model integrating network and text representation (SCR-NTR), which comprises data acquisition, feature representation, feature fusion and link prediction. We compare the network representation and text representation, respectively, and select the models performing best in the pre-experiment as the sub-models of SCR-NTR. The method of vector concatenate fusion is employed to fuse two kinds of information, and the logistic regression classifier is selected to carry out the link prediction. The extensive experiments reveal that our model can effectively improve the performance on citation recommendation. In addition, the effect of different fusion methods and different classifiers are investigated, and qualitative analysis is conducted to further verify the effectiveness of SCR-NTR. The experimental results show that leveraging both network and text representation can enhance the recommendation performance, and the heterogenous network representation learning can capture richer semantic information of the given network than the homogeneous one. [ABSTRACT FROM AUTHOR] |
| Copyright of Scientometrics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 153222879 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A scientific citation recommendation model integrating network and text representations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Qiu%2C+Tianshuang%22">Qiu, Tianshuang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Chuanming%22">Yu, Chuanming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yucm@zuel.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhong%2C+Yunci%22">Zhong, Yunci</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22An%2C+Lu%22">An, Lu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Gang%22">Li, Gang</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Scientometrics%22">Scientometrics</searchLink>. Nov2021, Vol. 126 Issue 11, p9199-9221. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Information+networks%22">Information networks</searchLink><br /><searchLink fieldCode="DE" term="%22Citation+networks%22">Citation networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The number of scientific papers is increasing in the rapid growth. How to make paper acquisition efficient and provide effective citation recommendation is essential for researchers. Although the application of scientific citation recommendation has shown great improvements, the in-depth mining and fusion of various types of information has been ignored. In this paper, we propose a scientific citation recommendation model integrating network and text representation (SCR-NTR), which comprises data acquisition, feature representation, feature fusion and link prediction. We compare the network representation and text representation, respectively, and select the models performing best in the pre-experiment as the sub-models of SCR-NTR. The method of vector concatenate fusion is employed to fuse two kinds of information, and the logistic regression classifier is selected to carry out the link prediction. The extensive experiments reveal that our model can effectively improve the performance on citation recommendation. In addition, the effect of different fusion methods and different classifiers are investigated, and qualitative analysis is conducted to further verify the effectiveness of SCR-NTR. The experimental results show that leveraging both network and text representation can enhance the recommendation performance, and the heterogenous network representation learning can capture richer semantic information of the given network than the homogeneous one. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Scientometrics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11192-021-04161-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 9199 Subjects: – SubjectFull: Logistic regression analysis Type: general – SubjectFull: Acquisition of data Type: general – SubjectFull: Information networks Type: general – SubjectFull: Citation networks Type: general Titles: – TitleFull: A scientific citation recommendation model integrating network and text representations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Qiu, Tianshuang – PersonEntity: Name: NameFull: Yu, Chuanming – PersonEntity: Name: NameFull: Zhong, Yunci – PersonEntity: Name: NameFull: An, Lu – PersonEntity: Name: NameFull: Li, Gang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 01389130 Numbering: – Type: volume Value: 126 – Type: issue Value: 11 Titles: – TitleFull: Scientometrics Type: main |
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