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Scholarly publication venue recommender systems: A systematic literature review

Hossein Dehdarirad (Department of Medical Library and Information Science, the School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran)
Javad Ghazimirsaeid (Department of Medical Library and Information Science, the School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran)
Ammar Jalalimanesh (Department of Industrial Engineering, Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran)

Data Technologies and Applications

ISSN: 2514-9288

Article publication date: 17 March 2020

Issue publication date: 2 June 2020

Abstract

Purpose

The purpose of this investigation is to identify, evaluate, integrate and summarize relevant and qualified papers through conducting a systematic literature review (SLR) on the application of recommender systems (RSs) to suggest a scholarly publication venue for researcher's paper.

Design/methodology/approach

To identify the relevant papers published up to August 11, 2018, an SLR study on four databases (Scopus, Web of Science, IEEE Xplore and ScienceDirect) was conducted. We pursued the guidelines presented by Kitchenham and Charters (2007) for performing SLRs in software engineering. The papers were analyzed based on data sources, RSs classes, techniques/methods/algorithms, datasets, evaluation methodologies and metrics, as well as future directions.

Findings

A total of 32 papers were identified. The most data sources exploited in these papers were textual (title/abstract/keywords) and co-authorship data. The RS classes in the selected papers were almost equally used. DBLP was the main dataset utilized. Cosine similarity, social network analysis (SNA) and term frequency–inverse document frequency (TF–IDF) algorithm were frequently used. In terms of evaluation methodologies, 24 papers applied only offline evaluations. Furthermore, precision, accuracy and recall metrics were the popular performance metrics. In the reviewed papers, “use more datasets” and “new algorithms” were frequently mentioned in the future work part as well as conclusions.

Originality/value

Given that a review study has not been conducted in this area, this paper can provide an insight into the current status in this area and may also contribute to future research in this field.

Keywords

Acknowledgements

This study was part of a PhD dissertation supported by Tehran University of Medical Sciences (No: IR.TUMS.SPH.REC.1397.117).

Citation

Dehdarirad, H., Ghazimirsaeid, J. and Jalalimanesh, A. (2020), "Scholarly publication venue recommender systems: A systematic literature review", Data Technologies and Applications, Vol. 54 No. 2, pp. 169-191. https://doi.org/10.1108/DTA-08-2019-0135

Publisher

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Emerald Publishing Limited

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