Scholarly recommendation systems: a literature survey
Abstract A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise...
Ausführliche Beschreibung
Autor*in: |
Zhang, Zitong [verfasserIn] |
---|
Format: |
Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2023 |
---|
Schlagwörter: |
---|
Anmerkung: |
© The Author(s) 2023 |
---|
Übergeordnetes Werk: |
Enthalten in: Knowledge and information systems - Springer London, 2000, 65(2023), 11 vom: 04. Juni, Seite 4433-4478 |
---|---|
Übergeordnetes Werk: |
volume:65 ; year:2023 ; number:11 ; day:04 ; month:06 ; pages:4433-4478 |
Links: |
---|
DOI / URN: |
10.1007/s10115-023-01901-x |
---|
Katalog-ID: |
OLC2145494871 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | OLC2145494871 | ||
003 | DE-627 | ||
005 | 20240118104957.0 | ||
007 | tu | ||
008 | 240118s2023 xx ||||| 00| ||eng c | ||
024 | 7 | |a 10.1007/s10115-023-01901-x |2 doi | |
035 | |a (DE-627)OLC2145494871 | ||
035 | |a (DE-He213)s10115-023-01901-x-p | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 004 |q VZ |
082 | 0 | 4 | |a 004 |q VZ |
084 | |a 06.74$jInformationssysteme |2 bkl | ||
084 | |a 54.64$jDatenbanken |2 bkl | ||
100 | 1 | |a Zhang, Zitong |e verfasserin |4 aut | |
245 | 1 | 0 | |a Scholarly recommendation systems: a literature survey |
264 | 1 | |c 2023 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
338 | |a Band |b nc |2 rdacarrier | ||
500 | |a © The Author(s) 2023 | ||
520 | |a Abstract A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain. | ||
650 | 4 | |a Scholarly recommendation systems | |
650 | 4 | |a Literature recommendation | |
650 | 4 | |a Collaborator recommendation | |
650 | 4 | |a Conference recommendation | |
650 | 4 | |a Journal recommendation | |
650 | 4 | |a Reviewer recommendation | |
700 | 1 | |a Patra, Braja Gopal |4 aut | |
700 | 1 | |a Yaseen, Ashraf |4 aut | |
700 | 1 | |a Zhu, Jie |4 aut | |
700 | 1 | |a Sabharwal, Rachit |4 aut | |
700 | 1 | |a Roberts, Kirk |4 aut | |
700 | 1 | |a Cao, Tru |4 aut | |
700 | 1 | |a Wu, Hulin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Knowledge and information systems |d Springer London, 2000 |g 65(2023), 11 vom: 04. Juni, Seite 4433-4478 |w (DE-627)323971725 |w (DE-600)2036569-X |w (DE-576)9323971723 |x 0219-1377 |7 nnns |
773 | 1 | 8 | |g volume:65 |g year:2023 |g number:11 |g day:04 |g month:06 |g pages:4433-4478 |
856 | 4 | 1 | |u https://doi.org/10.1007/s10115-023-01901-x |z lizenzpflichtig |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a SSG-OLC-MAT | ||
912 | |a SSG-OLC-BUB | ||
936 | b | k | |a 06.74$jInformationssysteme |q VZ |0 106415212 |0 (DE-625)106415212 |
936 | b | k | |a 54.64$jDatenbanken |q VZ |0 106410865 |0 (DE-625)106410865 |
951 | |a AR | ||
952 | |d 65 |j 2023 |e 11 |b 04 |c 06 |h 4433-4478 |
author_variant |
z z zz b g p bg bgp a y ay j z jz r s rs k r kr t c tc h w hw |
---|---|
matchkey_str |
article:02191377:2023----::coalrcmedtossesl |
hierarchy_sort_str |
2023 |
bklnumber |
06.74$jInformationssysteme 54.64$jDatenbanken |
publishDate |
2023 |
allfields |
10.1007/s10115-023-01901-x doi (DE-627)OLC2145494871 (DE-He213)s10115-023-01901-x-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Zhang, Zitong verfasserin aut Scholarly recommendation systems: a literature survey 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain. Scholarly recommendation systems Literature recommendation Collaborator recommendation Conference recommendation Journal recommendation Reviewer recommendation Patra, Braja Gopal aut Yaseen, Ashraf aut Zhu, Jie aut Sabharwal, Rachit aut Roberts, Kirk aut Cao, Tru aut Wu, Hulin aut Enthalten in Knowledge and information systems Springer London, 2000 65(2023), 11 vom: 04. Juni, Seite 4433-4478 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:65 year:2023 number:11 day:04 month:06 pages:4433-4478 https://doi.org/10.1007/s10115-023-01901-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 65 2023 11 04 06 4433-4478 |
spelling |
10.1007/s10115-023-01901-x doi (DE-627)OLC2145494871 (DE-He213)s10115-023-01901-x-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Zhang, Zitong verfasserin aut Scholarly recommendation systems: a literature survey 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain. Scholarly recommendation systems Literature recommendation Collaborator recommendation Conference recommendation Journal recommendation Reviewer recommendation Patra, Braja Gopal aut Yaseen, Ashraf aut Zhu, Jie aut Sabharwal, Rachit aut Roberts, Kirk aut Cao, Tru aut Wu, Hulin aut Enthalten in Knowledge and information systems Springer London, 2000 65(2023), 11 vom: 04. Juni, Seite 4433-4478 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:65 year:2023 number:11 day:04 month:06 pages:4433-4478 https://doi.org/10.1007/s10115-023-01901-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 65 2023 11 04 06 4433-4478 |
allfields_unstemmed |
10.1007/s10115-023-01901-x doi (DE-627)OLC2145494871 (DE-He213)s10115-023-01901-x-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Zhang, Zitong verfasserin aut Scholarly recommendation systems: a literature survey 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain. Scholarly recommendation systems Literature recommendation Collaborator recommendation Conference recommendation Journal recommendation Reviewer recommendation Patra, Braja Gopal aut Yaseen, Ashraf aut Zhu, Jie aut Sabharwal, Rachit aut Roberts, Kirk aut Cao, Tru aut Wu, Hulin aut Enthalten in Knowledge and information systems Springer London, 2000 65(2023), 11 vom: 04. Juni, Seite 4433-4478 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:65 year:2023 number:11 day:04 month:06 pages:4433-4478 https://doi.org/10.1007/s10115-023-01901-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 65 2023 11 04 06 4433-4478 |
allfieldsGer |
10.1007/s10115-023-01901-x doi (DE-627)OLC2145494871 (DE-He213)s10115-023-01901-x-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Zhang, Zitong verfasserin aut Scholarly recommendation systems: a literature survey 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain. Scholarly recommendation systems Literature recommendation Collaborator recommendation Conference recommendation Journal recommendation Reviewer recommendation Patra, Braja Gopal aut Yaseen, Ashraf aut Zhu, Jie aut Sabharwal, Rachit aut Roberts, Kirk aut Cao, Tru aut Wu, Hulin aut Enthalten in Knowledge and information systems Springer London, 2000 65(2023), 11 vom: 04. Juni, Seite 4433-4478 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:65 year:2023 number:11 day:04 month:06 pages:4433-4478 https://doi.org/10.1007/s10115-023-01901-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 65 2023 11 04 06 4433-4478 |
allfieldsSound |
10.1007/s10115-023-01901-x doi (DE-627)OLC2145494871 (DE-He213)s10115-023-01901-x-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Zhang, Zitong verfasserin aut Scholarly recommendation systems: a literature survey 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain. Scholarly recommendation systems Literature recommendation Collaborator recommendation Conference recommendation Journal recommendation Reviewer recommendation Patra, Braja Gopal aut Yaseen, Ashraf aut Zhu, Jie aut Sabharwal, Rachit aut Roberts, Kirk aut Cao, Tru aut Wu, Hulin aut Enthalten in Knowledge and information systems Springer London, 2000 65(2023), 11 vom: 04. Juni, Seite 4433-4478 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:65 year:2023 number:11 day:04 month:06 pages:4433-4478 https://doi.org/10.1007/s10115-023-01901-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 65 2023 11 04 06 4433-4478 |
language |
English |
source |
Enthalten in Knowledge and information systems 65(2023), 11 vom: 04. Juni, Seite 4433-4478 volume:65 year:2023 number:11 day:04 month:06 pages:4433-4478 |
sourceStr |
Enthalten in Knowledge and information systems 65(2023), 11 vom: 04. Juni, Seite 4433-4478 volume:65 year:2023 number:11 day:04 month:06 pages:4433-4478 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Scholarly recommendation systems Literature recommendation Collaborator recommendation Conference recommendation Journal recommendation Reviewer recommendation |
dewey-raw |
004 |
isfreeaccess_bool |
false |
container_title |
Knowledge and information systems |
authorswithroles_txt_mv |
Zhang, Zitong @@aut@@ Patra, Braja Gopal @@aut@@ Yaseen, Ashraf @@aut@@ Zhu, Jie @@aut@@ Sabharwal, Rachit @@aut@@ Roberts, Kirk @@aut@@ Cao, Tru @@aut@@ Wu, Hulin @@aut@@ |
publishDateDaySort_date |
2023-06-04T00:00:00Z |
hierarchy_top_id |
323971725 |
dewey-sort |
14 |
id |
OLC2145494871 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2145494871</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118104957.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2023 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10115-023-01901-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2145494871</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10115-023-01901-x-p</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">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">06.74$jInformationssysteme</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.64$jDatenbanken</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Zitong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Scholarly recommendation systems: a literature survey</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Scholarly recommendation systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Literature recommendation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Collaborator recommendation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Conference recommendation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Journal recommendation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reviewer recommendation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Patra, Braja Gopal</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yaseen, Ashraf</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Jie</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sabharwal, Rachit</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Roberts, Kirk</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cao, Tru</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Hulin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Knowledge and information systems</subfield><subfield code="d">Springer London, 2000</subfield><subfield code="g">65(2023), 11 vom: 04. Juni, Seite 4433-4478</subfield><subfield code="w">(DE-627)323971725</subfield><subfield code="w">(DE-600)2036569-X</subfield><subfield code="w">(DE-576)9323971723</subfield><subfield code="x">0219-1377</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:65</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:11</subfield><subfield code="g">day:04</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:4433-4478</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10115-023-01901-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">06.74$jInformationssysteme</subfield><subfield code="q">VZ</subfield><subfield code="0">106415212</subfield><subfield code="0">(DE-625)106415212</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.64$jDatenbanken</subfield><subfield code="q">VZ</subfield><subfield code="0">106410865</subfield><subfield code="0">(DE-625)106410865</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">65</subfield><subfield code="j">2023</subfield><subfield code="e">11</subfield><subfield code="b">04</subfield><subfield code="c">06</subfield><subfield code="h">4433-4478</subfield></datafield></record></collection>
|
author |
Zhang, Zitong |
spellingShingle |
Zhang, Zitong ddc 004 bkl 06.74$jInformationssysteme bkl 54.64$jDatenbanken misc Scholarly recommendation systems misc Literature recommendation misc Collaborator recommendation misc Conference recommendation misc Journal recommendation misc Reviewer recommendation Scholarly recommendation systems: a literature survey |
authorStr |
Zhang, Zitong |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)323971725 |
format |
Article |
dewey-ones |
004 - Data processing & computer science |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut aut aut |
collection |
OLC |
remote_str |
false |
illustrated |
Not Illustrated |
issn |
0219-1377 |
topic_title |
004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Scholarly recommendation systems: a literature survey Scholarly recommendation systems Literature recommendation Collaborator recommendation Conference recommendation Journal recommendation Reviewer recommendation |
topic |
ddc 004 bkl 06.74$jInformationssysteme bkl 54.64$jDatenbanken misc Scholarly recommendation systems misc Literature recommendation misc Collaborator recommendation misc Conference recommendation misc Journal recommendation misc Reviewer recommendation |
topic_unstemmed |
ddc 004 bkl 06.74$jInformationssysteme bkl 54.64$jDatenbanken misc Scholarly recommendation systems misc Literature recommendation misc Collaborator recommendation misc Conference recommendation misc Journal recommendation misc Reviewer recommendation |
topic_browse |
ddc 004 bkl 06.74$jInformationssysteme bkl 54.64$jDatenbanken misc Scholarly recommendation systems misc Literature recommendation misc Collaborator recommendation misc Conference recommendation misc Journal recommendation misc Reviewer recommendation |
format_facet |
Aufsätze Gedruckte Aufsätze |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
nc |
hierarchy_parent_title |
Knowledge and information systems |
hierarchy_parent_id |
323971725 |
dewey-tens |
000 - Computer science, knowledge & systems |
hierarchy_top_title |
Knowledge and information systems |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 |
title |
Scholarly recommendation systems: a literature survey |
ctrlnum |
(DE-627)OLC2145494871 (DE-He213)s10115-023-01901-x-p |
title_full |
Scholarly recommendation systems: a literature survey |
author_sort |
Zhang, Zitong |
journal |
Knowledge and information systems |
journalStr |
Knowledge and information systems |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
000 - Computer science, information & general works |
recordtype |
marc |
publishDateSort |
2023 |
contenttype_str_mv |
txt |
container_start_page |
4433 |
author_browse |
Zhang, Zitong Patra, Braja Gopal Yaseen, Ashraf Zhu, Jie Sabharwal, Rachit Roberts, Kirk Cao, Tru Wu, Hulin |
container_volume |
65 |
class |
004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl |
format_se |
Aufsätze |
author-letter |
Zhang, Zitong |
doi_str_mv |
10.1007/s10115-023-01901-x |
normlink |
106415212 106410865 |
normlink_prefix_str_mv |
106415212 (DE-625)106415212 106410865 (DE-625)106410865 |
dewey-full |
004 |
title_sort |
scholarly recommendation systems: a literature survey |
title_auth |
Scholarly recommendation systems: a literature survey |
abstract |
Abstract A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain. © The Author(s) 2023 |
abstractGer |
Abstract A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain. © The Author(s) 2023 |
abstract_unstemmed |
Abstract A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain. © The Author(s) 2023 |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB |
container_issue |
11 |
title_short |
Scholarly recommendation systems: a literature survey |
url |
https://doi.org/10.1007/s10115-023-01901-x |
remote_bool |
false |
author2 |
Patra, Braja Gopal Yaseen, Ashraf Zhu, Jie Sabharwal, Rachit Roberts, Kirk Cao, Tru Wu, Hulin |
author2Str |
Patra, Braja Gopal Yaseen, Ashraf Zhu, Jie Sabharwal, Rachit Roberts, Kirk Cao, Tru Wu, Hulin |
ppnlink |
323971725 |
mediatype_str_mv |
n |
isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1007/s10115-023-01901-x |
up_date |
2024-07-04T03:21:19.075Z |
_version_ |
1803617072937172992 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2145494871</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118104957.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2023 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10115-023-01901-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2145494871</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10115-023-01901-x-p</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">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">06.74$jInformationssysteme</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.64$jDatenbanken</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Zitong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Scholarly recommendation systems: a literature survey</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Scholarly recommendation systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Literature recommendation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Collaborator recommendation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Conference recommendation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Journal recommendation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reviewer recommendation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Patra, Braja Gopal</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yaseen, Ashraf</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Jie</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sabharwal, Rachit</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Roberts, Kirk</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cao, Tru</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Hulin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Knowledge and information systems</subfield><subfield code="d">Springer London, 2000</subfield><subfield code="g">65(2023), 11 vom: 04. Juni, Seite 4433-4478</subfield><subfield code="w">(DE-627)323971725</subfield><subfield code="w">(DE-600)2036569-X</subfield><subfield code="w">(DE-576)9323971723</subfield><subfield code="x">0219-1377</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:65</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:11</subfield><subfield code="g">day:04</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:4433-4478</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10115-023-01901-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">06.74$jInformationssysteme</subfield><subfield code="q">VZ</subfield><subfield code="0">106415212</subfield><subfield code="0">(DE-625)106415212</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.64$jDatenbanken</subfield><subfield code="q">VZ</subfield><subfield code="0">106410865</subfield><subfield code="0">(DE-625)106410865</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">65</subfield><subfield code="j">2023</subfield><subfield code="e">11</subfield><subfield code="b">04</subfield><subfield code="c">06</subfield><subfield code="h">4433-4478</subfield></datafield></record></collection>
|
score |
7.3991175 |