Abstract
There have been a massive number of conferences and journals in computer science that create a lot of difficulties for scientists, especially for early-stage researchers, to find the most suitable venue for their scientific submission. In this paper, we present a novel approach for building a paper submission recommendation system by using two different types of embedding methods, GloVe and FastText, as well as Convolutional Neural Network (CNN) and LSTM to extract useful features for a paper submission recommendation model. We consider seven combinations of initial attributes from a given submission: title, abstract, keywords, title + keyword, title + abstract, keyword + abstract, and title + keyword + abstract. We measure these approaches’ performance on one dataset, presented by Wang et al., in terms of top K accuracy and compare our methods with the S2RSCS model, the state-of-the-art algorithm on this dataset. The experimental results show that CNN + FastText can outperform other approaches (CNN + GloVe, LSTM + GloVe, LSTM + FastText, S2RSCS) in term of top 1 accuracy for seven types of input data. Without using a list of keywords in the input data, CNN + GloVe/FastText can surpass other techniques. It has a bit lower performance than S2RSCS in terms of the top 3 and top 5 accuracies when using the keyword information. Finally, the combination of S2RSCS and CNN + FastText, namely S2CFT, can create a better model that bypasses all other methods by top K accuracy (K = 1,3,5,10).
D. Nguyen and S. Huynh—Equal contribution.
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Acknowledgement
This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number NCM2019-18-01. We want to thank the University of Science, Vietnam National University in Ho Chi Minh City and AISIA Research Lab in Vietnam for supporting us throughout this paper.
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Nguyen, D., Huynh, S., Huynh, P., Dinh, C.V., Nguyen, B.T. (2021). S2CFT: A New Approach for Paper Submission Recommendation. In: Bureš, T., et al. SOFSEM 2021: Theory and Practice of Computer Science. SOFSEM 2021. Lecture Notes in Computer Science(), vol 12607. Springer, Cham. https://doi.org/10.1007/978-3-030-67731-2_41
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