TY - JOUR
T1 - Science Concierge
T2 - A fast content-based recommendation system for scientific publications
AU - Achakulvisut, Titipat
AU - Acuna, Daniel E.
AU - Ruangrong, Tulakan
AU - Kording, Konrad
N1 - Publisher Copyright:
© 2016 Achakulvisut et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2016/7/1
Y1 - 2016/7/1
N2 - 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.
AB - 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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U2 - 10.1371/journal.pone.0158423
DO - 10.1371/journal.pone.0158423
M3 - Article
C2 - 27383424
AN - SCOPUS:84978149949
SN - 1932-6203
VL - 11
JO - PloS one
JF - PloS one
IS - 7
M1 - e0158423
ER -