The design of a hybrid model-based journal recommendation system

There is currently an overload of information on the internet, and this makes information
search a challenging task. Researchers spend a lot of man-hour searching for journals
related to their areas of research interest that can publish their research output on time. In,
this study, a recommender system that can assist researchers access relevant journals that
can publish their research output on time based on their preferences is developed. This
system uses the information provided by researchers and previous authors' research
publications to recommend journals with similar preferences. Data were collected from 867
respondents through an online questionnaire and from existing publication sources and
databases on the web. The scope of the research was narrowed down to computer sciencerelated journals. A hybrid model-based recommendation approach that combined ContentBased and Collaborative filtering was employed for the study. The Naive Bayes and Random
Forest algorithms were used to model the recommender. WEKA, a machine learning tool,
was used to implement the system. The result of the study showed that the Naïve Bayes
produced a shorter training time (0.01s) and testing time (0.02s) than the Random forest
training time (0.41) and testing time (0.09). On the other hand, the classification accuracy
of the Random forest algorithm outperformed the naïve Bayes with % correctly classified
instance of 89.73 and 72.66; kappa of 0.893 and 0.714; True Positive of 0.897 and 0.727
and ROC area of 0.998 and 0.977, respectively, among other metrics. The model derived in
this work was used as a knowledge-base for the development of a web-based application,
named "Journal Recommender" which allowed academic authors to input their preferences
and obtain prompt journal recommendations. The developed system wo