A Comparative Analysis of Sentence Transformer Models for Automated Journal Recommendation Using PubMed Metadata

A Comparative Analysis of Sentence Transformer Models for Automated Journal Recommendation Using PubMed Metadata

We present an automated journal recommendation pipeline designed to evaluate the performance of five Sentence Transformer models—all-mpnet-base-v2 (Mpnet), all-MiniLM-L6-v2 (Minilm-l6), all-MiniLM-L12-v2 (Minilm-l12), multi-qa-distilbert-cos-v1 (Multi-qa-distilbert), and all-distilroberta-v1 (robert...

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Bibliographic Details
Main Authors: Maria Teresa Colangelo, Marco Meleti, Stefano Guizzardi, Elena Calciolari, Carlo Galli
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Big Data and Cognitive Computing
Subjects:
automated journal recommendation
KeyBERT
PubMed search
Sentence Transformers
semantic similarity
Online Access:https://www.mdpi.com/2504-2289/9/3/67
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