论文标题
FPSRS：纸张提交建议系统的融合方法
FPSRS: A Fusion Approach for Paper Submission Recommendation System
论文作者
论文摘要
推荐系统在娱乐和消费中越来越受欢迎，并且在学术界中很明显，特别是对于建议向科学家提交科学文章的应用。但是，由于不同出版商的各种接受率，影响因素和排名，寻找适当的场地或日记以提交科学工作通常需要大量时间和精力。在本文中，我们的目的是提出从我们的论文[13]中扩展出的两种较新的方法[13]，除了使用Cons1d之外，还采用了RNN结构，该论文[13]。此外，我们还引入了一种新方法，即Distilbertaims，使用Distillbert进行两种大写和低案例单词，以矢量化标题，摘要和关键字等功能，然后使用Conv1D执行特征提取。此外，我们提出了一种针对与其他功能的AIM和范围的相似性得分的新计算方法。这有助于保持相似性得分计算的权重连续更新，然后继续拟合更多数据。实验结果表明，第二种方法可以获得更好的性能，就顶部1的准确性而言，比以前的研究中最好的[13]高62.46％和12.44％。
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.
