An efficient approach for paper submission recommendation

Son, Huynh Thanh; Phong, Huynh Tan; Dac, Nguyen Huu

doi:10.1109/tencon50793.2020.9293909

Cited by 6 publications

(3 citation statements)

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“…They considered seven features groups: title, abstract, keywords, title + keyword, title + abstract, keyword + abstract, and title + keyword + abstract for training progress. The experimental results show that the combination of S2RSCS [17] and CNN + FastText, namely the proposed S2CFT [12] model has the best performance with the Top 1 accuracy is 68.11% when using a mixture of attribute title + keyword + abstract, the accuracy at Top 3, 5, and 10 are 90.8%, 96.25%, and 99.21% respectively.…”

Section: Related Workmentioning

confidence: 97%

“…Son and colleagues later developed and proposed a new approach to improve the paper submission recommendation algorithm's performance using other additional features. The proposed method in [17] uses TF-IDF, the Chi-square statistic, and the one-hot encoding technique to extract parts from available information in each paper. They applied two machine learning models, namely Logistics Linear Regression (LLR) and Multi-layer Perceptrons (MLP), to the different combinations of features from the paper submission.…”

Section: Related Workmentioning

confidence: 99%

“…Various well-known recommendation systems include the recommendation systems of Spotify or Netflix in streaming services, Amazon in e-commerce branches, Google, Facebook, and Youtube in media. Besides, the recommendation system in academics has gained importance in recent years, including the paper submission system [7,12,17,19], the knowledge-based recommendation system [16], and paper suggestion [1]. Selecting a suitable journal for submitting new work is not easy for almost researchers, including young and experienced people.…”

Section: Introductionmentioning

confidence: 99%

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SimCPSR: Simple Contrastive Learning for Paper Submission Recommendation System

Le¹,

Doan²,

Huynh³

et al. 2022

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The recommendation system plays a vital role in many areas, especially academic fields, to support researchers in submitting and increasing the acceptance of their work through the conference or journal selection process. This study proposes a transformer-based model using transfer learning as an efficient approach for the paper submission recommendation system. By combining essential information (such as the title, the abstract, and the list of keywords) with the aims & scopes of journals, the model can recommend the Top K journals that maximize the acceptance of the paper. Our model had developed through two states: (i) Fine-tuning the pre-trained language model (LM) with a simple contrastive learning framework. We utilized a simple supervised contrastive objective to fine-tune all parameters, encouraging the LM to learn the document representation effectively. (ii) The fine-tuned LM was then trained on different combinations of the features for the downstream task. This study suggests a more advanced method for enhancing the efficiency of the paper submission recommendation system compared to previous approaches when we respectively achieve 0.5173, 0.8097, 0.8862, 0.9496 for Top 1, 3, 5, 10 accuracies on the test set for combining the title, abstract, and keywords as input features. Incorporating the journals' aims and scopes, our model shows an exciting result by getting 0.5194, 0.8112, 0.8866, 0.9496 respective to Top 1, 3, 5, and 10. We provide the implementation and datasets for further reference at https://github.com/hduc-le/SimCPSR.

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Section: Related Workmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%