SimCPSR: Simple Contrastive Learning for Paper Submission Recommendation System

TL;DR

This paper introduces a transformer-based model utilizing contrastive learning to improve the accuracy of recommending suitable journals for academic paper submissions, leveraging multiple features including titles, abstracts, keywords, and journal aims.

Contribution

It presents a novel contrastive learning framework for fine-tuning language models to enhance paper-to-journal matching accuracy in recommendation systems.

Findings

Achieved top-1 accuracy of 0.5173 without journal aims.

Improved top-3 accuracy to 0.8097 with combined features.

Reaching top-10 accuracy of 0.9496 across experiments.

Abstract

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 and 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, and 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, and 0.9496 respective to Top 1, 3, 5, and 10.