Enhancing Thesis Reference Recommendation Using Multilingual BERT
TL;DR
A semantic-based recommendation system using Multilingual BERT (mBERT) to compute similarity between user queries and academic publication titles that effectively improves the relevance of academic reference recommendations compared to traditional approaches.
Abstract
The increasing number of students and research topics has led to challenges in identifying relevant and high-quality academic references efficiently. Traditional keyword-based search methods often fail to capture the semantic meaning of research topics, resulting in less accurate recommendations. This study proposes a semantic-based recommendation system using Multilingual BERT (mBERT) to compute similarity between user queries and academic publication titles. The system leverages text embeddings and cosine similarity to identify relevant references from the Semantic Scholar database. Experimental results show that the proposed approach achieves an accuracy of 90%, precision of 93.75%, recall of 93.75%, and F1-score of 0.9375 in semantic similarity classification. The results demonstrate that BERT-based semantic analysis effectively improves the relevance of academic reference recommendations compared to traditional approaches.