FPSRS: A Fusion Approach for Paper Submission Recommendation System

TL;DR

This paper introduces a fusion-based recommendation system for scientific paper submissions, utilizing advanced neural network techniques like RNN, Conv1D, and DistilBert to improve venue suggestion accuracy.

Contribution

It presents novel methods combining RNN, Conv1D, and DistilBert for paper submission recommendation, with an innovative similarity scoring approach that adapts over time.

Findings

Achieved 62.46% Top 1 accuracy, outperforming previous methods by 12.44%.

Demonstrated the effectiveness of combining DistilBert with Conv1D for feature extraction.

Proposed a dynamic similarity scoring method that updates continuously.

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.