S2RSCS: An Efficient Scientific Submission Recommendation System for Computer Science

Huynh, Son T.; Huynh, Phong T.; Nguyen, Dac H.; Cuong, Dinh V.; Nguyen, Binh T.

doi:10.1007/978-3-030-55789-8_17

Cited by 7 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2020, Dac et al [26] used a new technique for this problem by investigating numerous deep learning methods as LSTM [11], GRU [5], Conv1D, and the ensemble method. Interestingly, the experimental results [26] could outperform the previous results [12] in terms of Top 1, Top 3, Top 5, and Top 10 accuracies. However, the dataset's volume that Wang used has only 14012 samples, which is not large enough to be reliable or high confident results.…”

Section: Related Workmentioning

confidence: 74%

“…Wang used Chi-square and TF-IDF as feature engineering layers and a linear regression model for the classifier. Later, with the same data, with a simple deep learning approach [12], Son et al outweighed the performance of Wang's approach by using MLP (Multi-layer perceptron) as a classifier instead of using logistic regression with accuracy (Top 3) of 89.07%. In 2020, Dac et al [26] used a new technique for this problem by investigating numerous deep learning methods as LSTM [11], GRU [5], Conv1D, and the ensemble method.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

FPSRS: A Fusion Approach for Paper Submission Recommendation System

Huynh¹,

Dang²,

Nguyen³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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 in 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 lowercase words to vectorize features such as Title, Abstract, 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.

show abstract

Section: Related Workmentioning

confidence: 74%

Section: Related Workmentioning

confidence: 99%

FPSRS: A Fusion Approach for Paper Submission Recommendation System

Huynh¹,

Dang²,

Nguyen³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The accuracy of the classifier model for correctly recommending a journal within the top 3 suggestions was 0.6137. This algorithm was extended by Huynh et al [ 14 ] in their Scientific Submission Recommendation System for Computer Science (S2RSCS) algorithm using multilayer perceptrons as classifiers. Evaluated on the Wang et al’s [ 13 ] computer science data set, they achieved an accuracy of 0.8907 when using the title, abstract, and keywords as input to predict the top 3 computer science journals.…”

Section: Discussionmentioning

confidence: 99%

“…Wang et al’s [ 13 ] “publication recommender system” uses term frequency-inverse document frequency (TD-IDF), chi-squared feature selection, and softmax regression classification to suggest journals for computer science publications. This algorithm was subsequently extended by Huynh et al [ 14 ] using multilayer perceptrons as a classifier and by Nguyen et al [ 15 ] introducing a one-dimensional convolutional neural network. Accuracy measured on the same computer science data set showed increased improvement with the incorporation of artificial intelligence methods.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis

et al. 2023

View full text Add to dashboard Cite

Background Strategies to improve the selection of appropriate target journals may reduce delays in disseminating research results. Machine learning is increasingly used in content-based recommender algorithms to guide journal submissions for academic articles. Objective We sought to evaluate the performance of open-source artificial intelligence to predict the impact factor or Eigenfactor score tertile using academic article abstracts. Methods PubMed-indexed articles published between 2016 and 2021 were identified with the Medical Subject Headings (MeSH) terms “ophthalmology,” “radiology,” and “neurology.” Journals, titles, abstracts, author lists, and MeSH terms were collected. Journal impact factor and Eigenfactor scores were sourced from the 2020 Clarivate Journal Citation Report. The journals included in the study were allocated percentile ranks based on impact factor and Eigenfactor scores, compared with other journals that released publications in the same year. All abstracts were preprocessed, which included the removal of the abstract structure, and combined with titles, authors, and MeSH terms as a single input. The input data underwent preprocessing with the inbuilt ktrain Bidirectional Encoder Representations from Transformers (BERT) preprocessing library before analysis with BERT. Before use for logistic regression and XGBoost models, the input data underwent punctuation removal, negation detection, stemming, and conversion into a term frequency-inverse document frequency array. Following this preprocessing, data were randomly split into training and testing data sets with a 3:1 train:test ratio. Models were developed to predict whether a given article would be published in a first, second, or third tertile journal (0-33rd centile, 34th-66th centile, or 67th-100th centile), as ranked either by impact factor or Eigenfactor score. BERT, XGBoost, and logistic regression models were developed on the training data set before evaluation on the hold-out test data set. The primary outcome was overall classification accuracy for the best-performing model in the prediction of accepting journal impact factor tertile. Results There were 10,813 articles from 382 unique journals. The median impact factor and Eigenfactor score were 2.117 (IQR 1.102-2.622) and 0.00247 (IQR 0.00105-0.03), respectively. The BERT model achieved the highest impact factor tertile classification accuracy of 75.0%, followed by an accuracy of 71.6% for XGBoost and 65.4% for logistic regression. Similarly, BERT achieved the highest Eigenfactor score tertile classification accuracy of 73.6%, followed by an accuracy of 71.8% for XGBoost and 65.3% for logistic regression. Conclusions Open-source artificial intelligence can predict the impact factor and Eigenfactor score of accepting peer-reviewed journals. Further studies are required to examine the effect on publication success and the time-to-publication of such recommender systems.

show abstract

Comparing different search methods for the open access journal recommendation tool B!SON

et al. 2023

View full text Add to dashboard Cite

Finding a suitable open access journal to publish academic work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of predatory publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. A systematic requirements analysis was conducted in the form of a survey. The developed tool suggests open access journals based on title, abstract and references provided by the user. The recommendations are built on open data, publisher-independent and work across domains and languages. Transparency is provided by its open source nature, an open application programming interface (API) and by specifying which matches the shown recommendations are based on. The recommendation quality has been evaluated using two different evaluation techniques, including several new recommendation methods. We were able to improve the results from our previous paper with a pre-trained transformer model. The beta version of the tool received positive feedback from the community and in several test sessions. We developed a recommendation system for open access journals to help researchers find a suitable journal. The open tool has been extensively tested, and we found possible improvements for our current recommendation technique. Development by two German academic libraries ensures the longevity and sustainability of the system.

show abstract

S2RSCS: An Efficient Scientific Submission Recommendation System for Computer Science

Cited by 7 publications

References 10 publications

FPSRS: A Fusion Approach for Paper Submission Recommendation System

FPSRS: A Fusion Approach for Paper Submission Recommendation System

Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis

Comparing different search methods for the open access journal recommendation tool B!SON

Contact Info

Product

Resources

About