Research Article: “Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications”

The following article was recently published by PLOS ONE.
Title
Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications
Authors
Titipat Achakulvisut
Northwestern University
Daniel E. Acuna
Syracuse University
Tulakan Ruangrong
Mahidol University, Thailand
Konrad Kording
Rehabilitation Institute of Chicago
Source
PLOS ONE
Published: July 6, 2016
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.

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