EILEEN: A recommendation system for scientific publications and grants

TL;DR

EILEEN is an open-source recommendation system designed to enhance the discovery of scientific publications and grants by leveraging user behavior data and machine learning, significantly outperforming traditional methods.

Contribution

The paper introduces EILEEN, a novel recommendation system for scientific literature and grants that uses learning-to-rank with Random Forests and validated on real user data.

Findings

Random Forest ranking achieves an AUC of 0.9

EILEEN outperforms LSA and Elasticsearch baselines

System is publicly available at eileen.io

Abstract

Finding relevant scientific articles is crucial for advancing knowledge. Recommendation systems are helpful for such purpose, although they have only been applied to science recently. This article describes EILEEN (Exploratory Innovator of LitEraturE Networks), a recommendation system for scientific publications and grants with open source code and datasets. We describe EILEEN's architecture for ingesting and processing documents and modeling the recommendation system and keyphrase estimator. Using a unique dataset of log-in user behavior, we validate our recommendation system against Latent Semantic Analysis (LSA) and the standard ranking from Elasticsearch (Lucene scoring). We find that a learning-to-rank with Random Forest achieves an AUC of 0.9, significantly outperforming both baselines. Our results suggest that we can substantially improve science recommendations and learn about scientists' behavior through their search behavior. We make our system available through eileen.io