Science Concierge: A fast content-based recommendation system for scientific publications

TL;DR

This paper introduces a fast, content-based recommendation system for scientific publications, utilizing an algorithm and open-source Python library that outperforms keyword-based suggestions, aiding researchers in navigating large scholarly datasets.

Contribution

The authors developed a novel content-based recommendation algorithm and an open-source Python library tailored for scientific publications, demonstrating improved accuracy over keyword methods.

Findings

The system significantly outperforms keyword-based suggestions.

It achieves high correlation with human judgments.

The library is adaptable and suitable for real-time recommendations.

Abstract

Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.