A review of citation recommendation: from textual content to enriched context

Abstract Citation recommendation systems play an important role to alleviate the dilemma that scholar users spend a lot of time and experiences for literature survey. With the burgeoning computational models and open data movement, scientific repository can provide more evidence in support of recomm...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ma, Shutian [verfasserIn] Zhang, Chengzhi [verfasserIn] Liu, Xiaozhong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Citation recommendation Scientific paper recommendation Recommender system

Übergeordnetes Werk:	Enthalten in: Scientometrics - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978, 122(2020), 3 vom: 03. Jan., Seite 1445-1472
Übergeordnetes Werk:	volume:122 ; year:2020 ; number:3 ; day:03 ; month:01 ; pages:1445-1472

Links:	Volltext

DOI / URN:	10.1007/s11192-019-03336-0

Katalog-ID:	SPR017623995

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allfields	10.1007/s11192-019-03336-0 doi (DE-627)SPR017623995 (SPR)s11192-019-03336-0-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Ma, Shutian verfasserin aut A review of citation recommendation: from textual content to enriched context 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Citation recommendation systems play an important role to alleviate the dilemma that scholar users spend a lot of time and experiences for literature survey. With the burgeoning computational models and open data movement, scientific repository can provide more evidence in support of recommendation. On the one hand, recommenders are applying better algorithms to understand the text of user queries and candidate citations. On the other hand, more types of data such as citation network and co-author relationship are aggregated to enrich the citation contextual information. The available data used for recommendation has been extended from textual content to enriched context. This review is conducted to identify the information and methods used for recommendations recently. We begin by introducing definitions of the task, recommending factors along with the corresponding problems and some application platforms. Then, we classify existing recommenders according to user query types and review representative methods for each type. We also elaborate on different strategies applied in three main stages of citation recommendation. Finally, a few open issues for future investigations are proposed. Citation recommendation (dpeaa)DE-He213 Scientific paper recommendation (dpeaa)DE-He213 Recommender system (dpeaa)DE-He213 Zhang, Chengzhi verfasserin aut Liu, Xiaozhong verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 122(2020), 3 vom: 03. Jan., Seite 1445-1472 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:122 year:2020 number:3 day:03 month:01 pages:1445-1472 https://dx.doi.org/10.1007/s11192-019-03336-0 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 122 2020 3 03 01 1445-1472
spelling	10.1007/s11192-019-03336-0 doi (DE-627)SPR017623995 (SPR)s11192-019-03336-0-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Ma, Shutian verfasserin aut A review of citation recommendation: from textual content to enriched context 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Citation recommendation systems play an important role to alleviate the dilemma that scholar users spend a lot of time and experiences for literature survey. With the burgeoning computational models and open data movement, scientific repository can provide more evidence in support of recommendation. On the one hand, recommenders are applying better algorithms to understand the text of user queries and candidate citations. On the other hand, more types of data such as citation network and co-author relationship are aggregated to enrich the citation contextual information. The available data used for recommendation has been extended from textual content to enriched context. This review is conducted to identify the information and methods used for recommendations recently. We begin by introducing definitions of the task, recommending factors along with the corresponding problems and some application platforms. Then, we classify existing recommenders according to user query types and review representative methods for each type. We also elaborate on different strategies applied in three main stages of citation recommendation. Finally, a few open issues for future investigations are proposed. Citation recommendation (dpeaa)DE-He213 Scientific paper recommendation (dpeaa)DE-He213 Recommender system (dpeaa)DE-He213 Zhang, Chengzhi verfasserin aut Liu, Xiaozhong verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 122(2020), 3 vom: 03. Jan., Seite 1445-1472 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:122 year:2020 number:3 day:03 month:01 pages:1445-1472 https://dx.doi.org/10.1007/s11192-019-03336-0 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 122 2020 3 03 01 1445-1472
allfields_unstemmed	10.1007/s11192-019-03336-0 doi (DE-627)SPR017623995 (SPR)s11192-019-03336-0-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Ma, Shutian verfasserin aut A review of citation recommendation: from textual content to enriched context 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Citation recommendation systems play an important role to alleviate the dilemma that scholar users spend a lot of time and experiences for literature survey. With the burgeoning computational models and open data movement, scientific repository can provide more evidence in support of recommendation. On the one hand, recommenders are applying better algorithms to understand the text of user queries and candidate citations. On the other hand, more types of data such as citation network and co-author relationship are aggregated to enrich the citation contextual information. The available data used for recommendation has been extended from textual content to enriched context. This review is conducted to identify the information and methods used for recommendations recently. We begin by introducing definitions of the task, recommending factors along with the corresponding problems and some application platforms. Then, we classify existing recommenders according to user query types and review representative methods for each type. We also elaborate on different strategies applied in three main stages of citation recommendation. Finally, a few open issues for future investigations are proposed. Citation recommendation (dpeaa)DE-He213 Scientific paper recommendation (dpeaa)DE-He213 Recommender system (dpeaa)DE-He213 Zhang, Chengzhi verfasserin aut Liu, Xiaozhong verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 122(2020), 3 vom: 03. Jan., Seite 1445-1472 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:122 year:2020 number:3 day:03 month:01 pages:1445-1472 https://dx.doi.org/10.1007/s11192-019-03336-0 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 122 2020 3 03 01 1445-1472
allfieldsGer	10.1007/s11192-019-03336-0 doi (DE-627)SPR017623995 (SPR)s11192-019-03336-0-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Ma, Shutian verfasserin aut A review of citation recommendation: from textual content to enriched context 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Citation recommendation systems play an important role to alleviate the dilemma that scholar users spend a lot of time and experiences for literature survey. With the burgeoning computational models and open data movement, scientific repository can provide more evidence in support of recommendation. On the one hand, recommenders are applying better algorithms to understand the text of user queries and candidate citations. On the other hand, more types of data such as citation network and co-author relationship are aggregated to enrich the citation contextual information. The available data used for recommendation has been extended from textual content to enriched context. This review is conducted to identify the information and methods used for recommendations recently. We begin by introducing definitions of the task, recommending factors along with the corresponding problems and some application platforms. Then, we classify existing recommenders according to user query types and review representative methods for each type. We also elaborate on different strategies applied in three main stages of citation recommendation. Finally, a few open issues for future investigations are proposed. Citation recommendation (dpeaa)DE-He213 Scientific paper recommendation (dpeaa)DE-He213 Recommender system (dpeaa)DE-He213 Zhang, Chengzhi verfasserin aut Liu, Xiaozhong verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 122(2020), 3 vom: 03. Jan., Seite 1445-1472 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:122 year:2020 number:3 day:03 month:01 pages:1445-1472 https://dx.doi.org/10.1007/s11192-019-03336-0 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 122 2020 3 03 01 1445-1472
allfieldsSound	10.1007/s11192-019-03336-0 doi (DE-627)SPR017623995 (SPR)s11192-019-03336-0-e DE-627 ger DE-627 rakwb eng 050 370 ASE 31.00 bkl Ma, Shutian verfasserin aut A review of citation recommendation: from textual content to enriched context 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Citation recommendation systems play an important role to alleviate the dilemma that scholar users spend a lot of time and experiences for literature survey. With the burgeoning computational models and open data movement, scientific repository can provide more evidence in support of recommendation. On the one hand, recommenders are applying better algorithms to understand the text of user queries and candidate citations. On the other hand, more types of data such as citation network and co-author relationship are aggregated to enrich the citation contextual information. The available data used for recommendation has been extended from textual content to enriched context. This review is conducted to identify the information and methods used for recommendations recently. We begin by introducing definitions of the task, recommending factors along with the corresponding problems and some application platforms. Then, we classify existing recommenders according to user query types and review representative methods for each type. We also elaborate on different strategies applied in three main stages of citation recommendation. Finally, a few open issues for future investigations are proposed. Citation recommendation (dpeaa)DE-He213 Scientific paper recommendation (dpeaa)DE-He213 Recommender system (dpeaa)DE-He213 Zhang, Chengzhi verfasserin aut Liu, Xiaozhong verfasserin aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 122(2020), 3 vom: 03. Jan., Seite 1445-1472 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:122 year:2020 number:3 day:03 month:01 pages:1445-1472 https://dx.doi.org/10.1007/s11192-019-03336-0 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 122 2020 3 03 01 1445-1472
language	English
source	Enthalten in Scientometrics 122(2020), 3 vom: 03. Jan., Seite 1445-1472 volume:122 year:2020 number:3 day:03 month:01 pages:1445-1472
sourceStr	Enthalten in Scientometrics 122(2020), 3 vom: 03. Jan., Seite 1445-1472 volume:122 year:2020 number:3 day:03 month:01 pages:1445-1472
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Citation recommendation Scientific paper recommendation Recommender system
dewey-raw	050
isfreeaccess_bool	false
container_title	Scientometrics
authorswithroles_txt_mv	Ma, Shutian @@aut@@ Zhang, Chengzhi @@aut@@ Liu, Xiaozhong @@aut@@
publishDateDaySort_date	2020-01-03T00:00:00Z
hierarchy_top_id	320589099
dewey-sort	250
id	SPR017623995
language_de	englisch
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author	Ma, Shutian
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title	A review of citation recommendation: from textual content to enriched context
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title_full	A review of citation recommendation: from textual content to enriched context
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title_sort	review of citation recommendation: from textual content to enriched context
title_auth	A review of citation recommendation: from textual content to enriched context
abstract	Abstract Citation recommendation systems play an important role to alleviate the dilemma that scholar users spend a lot of time and experiences for literature survey. With the burgeoning computational models and open data movement, scientific repository can provide more evidence in support of recommendation. On the one hand, recommenders are applying better algorithms to understand the text of user queries and candidate citations. On the other hand, more types of data such as citation network and co-author relationship are aggregated to enrich the citation contextual information. The available data used for recommendation has been extended from textual content to enriched context. This review is conducted to identify the information and methods used for recommendations recently. We begin by introducing definitions of the task, recommending factors along with the corresponding problems and some application platforms. Then, we classify existing recommenders according to user query types and review representative methods for each type. We also elaborate on different strategies applied in three main stages of citation recommendation. Finally, a few open issues for future investigations are proposed.
abstractGer	Abstract Citation recommendation systems play an important role to alleviate the dilemma that scholar users spend a lot of time and experiences for literature survey. With the burgeoning computational models and open data movement, scientific repository can provide more evidence in support of recommendation. On the one hand, recommenders are applying better algorithms to understand the text of user queries and candidate citations. On the other hand, more types of data such as citation network and co-author relationship are aggregated to enrich the citation contextual information. The available data used for recommendation has been extended from textual content to enriched context. This review is conducted to identify the information and methods used for recommendations recently. We begin by introducing definitions of the task, recommending factors along with the corresponding problems and some application platforms. Then, we classify existing recommenders according to user query types and review representative methods for each type. We also elaborate on different strategies applied in three main stages of citation recommendation. Finally, a few open issues for future investigations are proposed.
abstract_unstemmed	Abstract Citation recommendation systems play an important role to alleviate the dilemma that scholar users spend a lot of time and experiences for literature survey. With the burgeoning computational models and open data movement, scientific repository can provide more evidence in support of recommendation. On the one hand, recommenders are applying better algorithms to understand the text of user queries and candidate citations. On the other hand, more types of data such as citation network and co-author relationship are aggregated to enrich the citation contextual information. The available data used for recommendation has been extended from textual content to enriched context. This review is conducted to identify the information and methods used for recommendations recently. We begin by introducing definitions of the task, recommending factors along with the corresponding problems and some application platforms. Then, we classify existing recommenders according to user query types and review representative methods for each type. We also elaborate on different strategies applied in three main stages of citation recommendation. Finally, a few open issues for future investigations are proposed.
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title_short	A review of citation recommendation: from textual content to enriched context
url	https://dx.doi.org/10.1007/s11192-019-03336-0
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up_date	2024-07-03T14:06:43.532Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR017623995</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111052948.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11192-019-03336-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR017623995</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11192-019-03336-0-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">050</subfield><subfield code="a">370</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ma, Shutian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A review of citation recommendation: from textual content to enriched context</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Citation recommendation systems play an important role to alleviate the dilemma that scholar users spend a lot of time and experiences for literature survey. With the burgeoning computational models and open data movement, scientific repository can provide more evidence in support of recommendation. On the one hand, recommenders are applying better algorithms to understand the text of user queries and candidate citations. On the other hand, more types of data such as citation network and co-author relationship are aggregated to enrich the citation contextual information. The available data used for recommendation has been extended from textual content to enriched context. This review is conducted to identify the information and methods used for recommendations recently. We begin by introducing definitions of the task, recommending factors along with the corresponding problems and some application platforms. Then, we classify existing recommenders according to user query types and review representative methods for each type. We also elaborate on different strategies applied in three main stages of citation recommendation. 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A review of citation recommendation: from textual content to enriched context

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