Emati: a recommender system for biomedical literature based on supervised learning

Abstract

The scientific literature continues to grow at an ever-increasing rate. Considering that thousands of new articles are published every week, it is obvious how challenging it is to keep up with newly published literature on a regular basis. Using a recommender system that improves the user experience in the online environment can be a solution to this problem. In the present study, we aimed to develop a web-based article recommender service, called Emati. Since the data are text-based by nature and we wanted our system to be independent of the number of users, a content-based approach has been adopted in this study. A supervised machine learning model has been proposed to generate article recommendations. Two different supervised learning approaches, namely the naïve Bayes model with Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer and the state-of-the-art language model bidirectional encoder representations from transformers (BERT), have been implemented. In the first one, a list of documents is converted into TF-IDF–weighted features and fed into a classifier to distinguish relevant articles from irrelevant ones. Multinomial naïve Bayes algorithm is used as a classifier since, along with the class label, it also gives the probability that the input belongs to this class. The second approach is based on fine-tuning the pretrained state-of-the-art language model BERT for the text classification task. Emati provides a weekly updated list of article recommendations and presents it to the user, sorted by probability scores. New article recommendations are also sent to users’ email addresses on a weekly basis. Additionally, Emati has a personalized search feature to search online services’ (such as PubMed and arXiv) content and have the results sorted by the user’s classifier. Database URL: https://emati.biotec.tu-dresden.de

Large Language Models to process, analyze, and synthesize biomedical texts – a scoping review

“…Information retrieval focuses on enabling users to find and access the specific information they need from among vast amounts of available content. Various applications have been developed to improve the discovery of novel insights by more effectively aggregating data from existing knowledge databases, as well as improving article recommendation service [23, 24]. For instance, pubmedKB serves as a web server designed to extract and visualize semantic relationships between genes, diseases, chemicals, and variants within PubMed abstracts [25].…”