Abstract:
With the exponentially increasing journals being published every year, researchers
need support in choosing the most fitting journal in submitting articles. To address
this problem, this study developed a recommender system for journals with a
content-based component to compare the text-based similarities between an input
article and an already published journal articles in a database. The recommender
system also includes a knowledge-based component, to assess the publication
requirements of the researcher. This recommender system assists researchers of
social sciences and medicine, to find appropriate publication venues from the open
access journals. This research evaluated 16 factors that could influence the selection
of journals. A survey was conducted to find out the importance of the 16 journal
selection factors from the researcher’s point of view. Subsequently, suitability of
five algorithms was studied to find the most appropriate algorithm to implement the
content-based component. According to the results, BM25 similarity surpassed the
other algorithms that were studied. A knowledge-based component was developed
to combine with the content-based component. Knowledge-based component
organizes the order of journals recommended by the content-based component which
is based on researcher’s requirements of journal selection factors. A second survey
was conducted to find whether and to what extent researchers considered these
journal factors when choosing a suitable journal for in publishing their recent
articles. A third survey requested the participants of the second survey to rank the
suitability of journals recommended by the combined recommender system. The
results revealed that about 58.8% of researchers from Social Sciences and 66.2% of
researchers from Medicine, agree with the suggestions made by the combined
recommender system. Furthermore, 40.4% of Social Sciences and 35.5% of
Medicine researchers have recommended more suitable journal(s) than the one they
have already published in. Average performance of the recommender indicated that
about 18% and 15% of the researchers in Social Sciences and Medicine respectively
have lost the similar recommendations, according to the most suitable order.
Percentages were indicated as 28.4% and 22.4% of loss in Social Sciences and
Medicine respectively when the average performance was scrutinized with a system
that recommends suitable suggestion for all 10 topmost retrievals according to the
most suitable order. The end result of this study is applicable to publishers of
journals, editors, policy developers of academic organizations, librarians, and system
developers, in addition to researchers.
