Abstract
Recommender systems have been increasingly popular in entertainment and consumption and are evident in academics, especially for applications that suggest submitting scientific articles to scientists. However, because of the various acceptance rates, impact factors, and rankings in different publishers, searching for a proper venue or journal to submit a scientific work usually takes a lot of time and effort. In this paper, we aim to present two newer approaches extended from our paper [13] presented at the conference IAE/AIE 2021 by employing RNN structures besides using Conv1D. In addition, we also introduce a new method, namely DistilBertAims, using DistillBert for two cases of uppercase and lower-case words to vectorize features such as Title, Abstract, and Keywords, and then use Conv1d to perform feature extraction. Furthermore, we propose a new calculation method for similarity score for Aim & Scope with other features; this helps keep the weights of similarity score calculation continuously updated and then continue to fit more data. The experimental results show that the second approach could obtain a better performance, which is 62.46% and 12.44% higher than the best of the previous study [13] in terms of the Top 1 accuracy.
Abstract (translated)
URL
https://arxiv.org/abs/2205.05965
