Recommender Systems and Techniques

This paper develops a Transformer-based tool recommender system for Galaxy, achieving faster convergence and better generalization than a previous RNN-based system.

The paper develops a deep learning model to recommend Galaxy tools based on workflow analysis, achieving 97% accuracy in tool prediction.

This paper introduces a journal recommendation model for human neuroimaging studies that uses brain activation patterns to suggest relevant journals.