A scientific citation recommendation model integrating network and text representations
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 mi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Qiu, Tianshuang [verfasserIn] Yu, Chuanming [verfasserIn] Zhong, Yunci [verfasserIn] An, Lu [verfasserIn] Li, Gang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© Akadémiai Kiadó, Budapest, Hungary 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Scientometrics - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978, 126(2021), 11 vom: 26. Sept., Seite 9199-9221 |
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| Übergeordnetes Werk: |
volume:126 ; year:2021 ; number:11 ; day:26 ; month:09 ; pages:9199-9221 |
| Links: |
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| DOI / URN: |
10.1007/s11192-021-04161-0 |
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| Katalog-ID: |
SPR045402787 |
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| 520 | |a 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. | ||
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| 650 | 4 | |a Text representation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Link prediction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Citation network |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Yu, Chuanming |e verfasserin |4 aut | |
| 700 | 1 | |a Zhong, Yunci |e verfasserin |4 aut | |
| 700 | 1 | |a An, Lu |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Gang |e verfasserin |4 aut | |
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10.1007/s11192-021-04161-0 doi (DE-627)SPR045402787 (SPR)s11192-021-04161-0-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Qiu, Tianshuang verfasserin aut A scientific citation recommendation model integrating network and text representations 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2021 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. Scientific citation recommendation (dpeaa)DE-He213 Network representation (dpeaa)DE-He213 Text representation (dpeaa)DE-He213 Link prediction (dpeaa)DE-He213 Citation network (dpeaa)DE-He213 Yu, Chuanming verfasserin aut Zhong, Yunci verfasserin aut An, Lu verfasserin aut Li, Gang verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 126(2021), 11 vom: 26. Sept., Seite 9199-9221 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:126 year:2021 number:11 day:26 month:09 pages:9199-9221 https://dx.doi.org/10.1007/s11192-021-04161-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 126 2021 11 26 09 9199-9221 |
| spelling |
10.1007/s11192-021-04161-0 doi (DE-627)SPR045402787 (SPR)s11192-021-04161-0-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Qiu, Tianshuang verfasserin aut A scientific citation recommendation model integrating network and text representations 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2021 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. Scientific citation recommendation (dpeaa)DE-He213 Network representation (dpeaa)DE-He213 Text representation (dpeaa)DE-He213 Link prediction (dpeaa)DE-He213 Citation network (dpeaa)DE-He213 Yu, Chuanming verfasserin aut Zhong, Yunci verfasserin aut An, Lu verfasserin aut Li, Gang verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 126(2021), 11 vom: 26. Sept., Seite 9199-9221 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:126 year:2021 number:11 day:26 month:09 pages:9199-9221 https://dx.doi.org/10.1007/s11192-021-04161-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 126 2021 11 26 09 9199-9221 |
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10.1007/s11192-021-04161-0 doi (DE-627)SPR045402787 (SPR)s11192-021-04161-0-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Qiu, Tianshuang verfasserin aut A scientific citation recommendation model integrating network and text representations 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2021 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. Scientific citation recommendation (dpeaa)DE-He213 Network representation (dpeaa)DE-He213 Text representation (dpeaa)DE-He213 Link prediction (dpeaa)DE-He213 Citation network (dpeaa)DE-He213 Yu, Chuanming verfasserin aut Zhong, Yunci verfasserin aut An, Lu verfasserin aut Li, Gang verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 126(2021), 11 vom: 26. Sept., Seite 9199-9221 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:126 year:2021 number:11 day:26 month:09 pages:9199-9221 https://dx.doi.org/10.1007/s11192-021-04161-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 126 2021 11 26 09 9199-9221 |
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10.1007/s11192-021-04161-0 doi (DE-627)SPR045402787 (SPR)s11192-021-04161-0-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Qiu, Tianshuang verfasserin aut A scientific citation recommendation model integrating network and text representations 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2021 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. Scientific citation recommendation (dpeaa)DE-He213 Network representation (dpeaa)DE-He213 Text representation (dpeaa)DE-He213 Link prediction (dpeaa)DE-He213 Citation network (dpeaa)DE-He213 Yu, Chuanming verfasserin aut Zhong, Yunci verfasserin aut An, Lu verfasserin aut Li, Gang verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 126(2021), 11 vom: 26. Sept., Seite 9199-9221 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:126 year:2021 number:11 day:26 month:09 pages:9199-9221 https://dx.doi.org/10.1007/s11192-021-04161-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 126 2021 11 26 09 9199-9221 |
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10.1007/s11192-021-04161-0 doi (DE-627)SPR045402787 (SPR)s11192-021-04161-0-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Qiu, Tianshuang verfasserin aut A scientific citation recommendation model integrating network and text representations 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2021 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. Scientific citation recommendation (dpeaa)DE-He213 Network representation (dpeaa)DE-He213 Text representation (dpeaa)DE-He213 Link prediction (dpeaa)DE-He213 Citation network (dpeaa)DE-He213 Yu, Chuanming verfasserin aut Zhong, Yunci verfasserin aut An, Lu verfasserin aut Li, Gang verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 126(2021), 11 vom: 26. Sept., Seite 9199-9221 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:126 year:2021 number:11 day:26 month:09 pages:9199-9221 https://dx.doi.org/10.1007/s11192-021-04161-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 126 2021 11 26 09 9199-9221 |
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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. 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Qiu, Tianshuang |
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scientific citation recommendation model integrating network and text representations |
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A scientific citation recommendation model integrating network and text representations |
| abstract |
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. © Akadémiai Kiadó, Budapest, Hungary 2021 |
| abstractGer |
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. © Akadémiai Kiadó, Budapest, Hungary 2021 |
| abstract_unstemmed |
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. © Akadémiai Kiadó, Budapest, Hungary 2021 |
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A scientific citation recommendation model integrating network and text representations |
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https://dx.doi.org/10.1007/s11192-021-04161-0 |
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Yu, Chuanming Zhong, Yunci An, Lu Li, Gang |
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Yu, Chuanming Zhong, Yunci An, Lu Li, Gang |
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| doi_str |
10.1007/s11192-021-04161-0 |
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2024-07-03T15:45:58.946Z |
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| score |
7.3982153 |