Details
| Original language | English |
|---|---|
| Title of host publication | Linking Theory and Practice of Digital Libraries |
| Editors | Omar Alonso, Helena Cousijn, Gianmaria Silvello, Stefano Marchesin, Mónica Marrero, Carla Teixeira Lopes |
| Pages | 230-238 |
| Number of pages | 9 |
| ISBN (electronic) | 978-3-031-43849-3 |
| Publication status | Published - 2023 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 14241 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Keywords
- Recommender systems, Scientific publishing
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Linking Theory and Practice of Digital Libraries. ed. / Omar Alonso; Helena Cousijn; Gianmaria Silvello; Stefano Marchesin; Mónica Marrero; Carla Teixeira Lopes. 2023. p. 230-238 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14241 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A Comparison of Automated Journal Recommender Systems
AU - Entrup, Elias
AU - Ewerth, Ralph
AU - Hoppe, Anett
N1 - Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Choosing the right journal for an article can be a challenge. Automated manuscript matching can help authors with the decision by recommending suitable journals based on user-defined criteria. Several approaches for efficient matching have been proposed in the research literature. However, only a few actual recommender systems are available for end users. In this paper, we present an overview of available services and compare their key characteristics such as input values, functionalities, and privacy. We conduct a quantitative analysis of their recommendation results: (a) examining the overlap in the results and pointing out the similarities among them; (b) evaluating their quality with a comparison of their accuracy. Due to the providers’ lack of transparency about the used technologies, the results cannot be easily interpreted. This highlights the need for openness about the used algorithms and data sets.
AB - Choosing the right journal for an article can be a challenge. Automated manuscript matching can help authors with the decision by recommending suitable journals based on user-defined criteria. Several approaches for efficient matching have been proposed in the research literature. However, only a few actual recommender systems are available for end users. In this paper, we present an overview of available services and compare their key characteristics such as input values, functionalities, and privacy. We conduct a quantitative analysis of their recommendation results: (a) examining the overlap in the results and pointing out the similarities among them; (b) evaluating their quality with a comparison of their accuracy. Due to the providers’ lack of transparency about the used technologies, the results cannot be easily interpreted. This highlights the need for openness about the used algorithms and data sets.
KW - Recommender systems
KW - Scientific publishing
UR - http://www.scopus.com/inward/record.url?scp=85174563327&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-43849-3_20
DO - 10.1007/978-3-031-43849-3_20
M3 - Conference contribution
SN - 978-3-031-43848-6
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 230
EP - 238
BT - Linking Theory and Practice of Digital Libraries
A2 - Alonso, Omar
A2 - Cousijn, Helena
A2 - Silvello, Gianmaria
A2 - Marchesin, Stefano
A2 - Marrero, Mónica
A2 - Teixeira Lopes, Carla
ER -