Scholarly publication venue recommender systems. A systematic literature review

Date17 March 2020
Publication Date17 March 2020
AuthorHossein Dehdarirad,Javad Ghazimirsaeid,Ammar Jalalimanesh
SubjectLibrary & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Metadata,Information & knowledge management,Information & communications technology,Internet
Scholarly publication venue
recommender systems
A systematic literature review
Hossein Dehdarirad and Javad Ghazimirsaeid
Department of Medical Library and Information Science,
the School of Allied Medical Sciences, Tehran University of Medical Sciences,
Tehran, Iran, and
Ammar Jalalimanesh
Department of Industrial Engineering,
Iranian Research Institute for Information Science and Technology (IranDoc),
Tehran, Iran
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 researchers 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
frequencyinverse document frequency (TFIDF) 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 datasetsand new algorithmswere
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 Recommender systems, Recommendation systems, Venue, Journal, Conference
Paper type Literature review
1. Introduction
Researchers, scholars and students are mandated to publish more regarding their research
activities in order to receive grant, academic promotion and to be successfully graduated in
their courses. To meet the ever challenging need of publication activities, thousands of new
publishers have come up globally, and the number of online and subscribed journals has
increased massively (Abdollahi et al., 2014). According to the Scopus database, between 1992
and 2002, about 12 million papers have been published and this number has doubled between
2003 and 2013 (Kang et al., 2015). Also, only in the Scopus database in 2017 more than 22,800
active journals were indexed (Scopus, 2017).
One of the main goals of researchers is to publish a paper in a journal or a conference
(Medvet et al., 2014). Therefore, finding and choosing an appropriate publication venue to
This study was part of a PhD dissertation supported by Tehran University of Medical Sciences (No: IR.
The current issue and full text archive of this journal is available on Emerald Insight at:
Received 14 August 2019
Revised 28 December 2019
Accepted 6 February 2020
Data Technologies and
Vol. 54 No. 2, 2020
pp. 169-191
© Emerald Publishing Limited
DOI 10.1108/DTA-08-2019-0135
submit a paper is one of the main steps in paper publishing process (Kang et al., 2015).
However, one of the common problems among researchers is confusion about choosing the
right publication venue to submit their papers, especially for novice researchers. This
situation may occur as many publication venues have a very wide diversity of topics, and
many papers contain multiple academic disciplines. These issues usually cause the paper to
be rejected which will lead to a waste of time and cost (Kang et al., 2015;Silva et al., 2015).
Recommender systems (RSs) are one the solutions in helping users to manage information
overload (Luong et al., 2012a). The RSs as an independent research field emerged in the mid-
1990s, and in recent years interest in this area has considerably increased. These systems are
software tools and techniques that help users to find the most related items to their interests
(Ricci et al., 2015).
For researchers, a system that automatically provides relevant scholarly publication
venues considering user profile and user research interest may be very useful. Hence, RSs can
play an important role for researchers and scholars in their research projects (Medvet et al.,
2014). Although numerous papers have been published in this area, to the best of our
knowledge there is no review article. Therefore, the aim of this study is to accomplish a
systematic literature review (SLR) on scholarly publication venue RSs papers.
This paper is arranged as follows: section 2 presents background and related works.
Methodology is presented in section 3. The results of this study are presented in section 4.
Finally, discussion and conclusionsof the results are presented in section5 and 6 respectively.
2. Background and related works
RSs are believed to be helpful for people to identify the contents of their interests among
significant available options. By inventing the term of collaborative filteringsince 1990s,
importantresearches have been carriedout which made people to be aware of the need for RSs
eras et al.,2015). RSs are software tools and techniques which provide recommendations of
items to users. Theseitems can be in different types, for example, songs, places, news, books,
films and events (Figueroa et al., 2015). Several studies have classified RSs based on filtering
approachinto different classes,e.g. Burke (2002) categorizedRSs into five classes:collaborative,
content-based,demographic, utility-based and knowledge-based. Aggarwal (2016) considered
five classes for RSs:collaborative, content-based,knowledge-based, demographic andhybrid.
In the present study RSs are classified into three main categories which are: content-based
filtering, collaborative filtering and hybrid filtering systems (Rollins et al., 2017).
RSs in scholarly communities have been used in number of fields such as paper
recommendation (Haruna et al., 2017;Gori and Pucci, 2006;Chenguang and Wenxin, 2010;
Zhiping and Linna, 2010;Beel et al., 2013;Mishra, 2012;Bancu et al., 2012), expert and
reviewer recommendation (Reichling and Wulf, 2009;Hoang et al., 2018;Protasiewicz et al.,
2016;KardanA and Aghahadi, 2013;Davoodi et al., 2013;Yunhong and Xianli, 2016). To the
best of our knowledge no SLR study has been conducted on scholarly publication venue RSs
papers. However, few review studies in the field of RSs have been published which are
presented as below.
Champiri et al. (2015) undertook a systematic review to identify the contextual information
and methods used for accomplishing recommendations in digital libraries from 2001 to 2013.
They also examined the way in which researchers understood and used relevant contextual
information. Their findings showed that contextual information, when integrated into
recommendations, can be classified into three contexts, namely userscontext, documents
context and environment context. Additionally, collaborative filtering as a classical approach
was used more than the other approaches. Researchers have understood and used relevant
contextual information through four ways, including citation of past studies, citation of past
definitions, self-definitions and field-query researches, in which the citation of past studies
was the most popular method.

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