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