S2CFT: A New Approach for Paper Submission Recommendation

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).