November 30, 2021

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.

Direct to Full Text Article ||| PDF Version (pages)

About Gary Price

Gary Price (gprice@mediasourceinc.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. Before launching INFOdocket, Price and Shirl Kennedy were the founders and senior editors at ResourceShelf and DocuTicker for 10 years. From 2006-2009 he was Director of Online Information Services at Ask.com, and is currently a contributing editor at Search Engine Land.

Share