Abstract
With the increasing number of scientific publications as well as conferences and journals, it is often hard for researchers (especially newcomers) to find a suitable venue to present their studies. A submission recommendation system would be hugely helpful to assist authors in deciding where they can submit their work. In this paper, we propose a novel approach for a Scientific Submission Recommendation System for Computer Science (S2RSCS) by using the necessary information from the title, the abstract, and the list of keywords in given paper submission. By using tf-idf, the chi-square statistics, and the one-hot encoding technique, we consider different schemes for feature selection, which can be extracted from the title, the abstract, and keywords, to generate various groups of features. We investigate two machine-learning models, including Logistic Linear Regression (LLR) and Multi-layer Perceptrons (MLP), for constructing an appropriate recommendation engine. The experimental results show that using keywords can help to increase the performance of the recommendation model significantly. Prominently, the proposed methods outperform the previous work [1] for different groups of features in terms of top-3 accuracy. These results can give a promising contribution to the current research of the paper recommendation topic.
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Acknowledgement
We would like to thank The National Foundation for Science and Technology Development (NAFOSTED), University of Science, Inspectorio Research Lab, and AISIA Research Lab for supporting us throughout this paper.
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Huynh, S.T., Huynh, P.T., Nguyen, D.H., Cuong, D.V., Nguyen, B.T. (2020). S2RSCS: An Efficient Scientific Submission Recommendation System for Computer Science. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_17
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DOI: https://doi.org/10.1007/978-3-030-55789-8_17
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