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Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications

Author

Listed:
  • Titipat Achakulvisut
  • Daniel E Acuna
  • Tulakan Ruangrong
  • Konrad Kording

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.

Suggested Citation

  • Titipat Achakulvisut & Daniel E Acuna & Tulakan Ruangrong & Konrad Kording, 2016. "Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-11, July.
    • Handle: RePEc:plo:pone00:0158423
    DOI: 10.1371/journal.pone.0158423
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    Citations

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    Cited by:

    1. Krzysztof Przystupa & Mykola Beshley & Olena Hordiichuk-Bublivska & Marian Kyryk & Halyna Beshley & Julia Pyrih & Jarosław Selech, 2021. "Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems," Energies, MDPI, vol. 14(8), pages 1-24, April.
    2. Tianshuang Qiu & Chuanming Yu & Yunci Zhong & Lu An & Gang Li, 2021. "A scientific citation recommendation model integrating network and text representations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9199-9221, November.
    3. Yonghe Lu & Jiayi Luo & Ying Xiao & Hou Zhu, 2021. "Text representation model of scientific papers based on fusing multi-viewpoint information and its quality assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6937-6963, August.
    4. Zafar Ali & Guilin Qi & Pavlos Kefalas & Shah Khusro & Inayat Khan & Khan Muhammad, 2022. "SPR-SMN: scientific paper recommendation employing SPECTER with memory network," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6763-6785, November.
    5. Yiqin Lv & Zheng Xie & Xiaojing Zuo & Yiping Song, 2022. "A multi-view method of scientific paper classification via heterogeneous graph embeddings," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4847-4872, August.
    6. Zafar Ali & Irfan Ullah & Amin Khan & Asim Ullah Jan & Khan Muhammad, 2021. "An overview and evaluation of citation recommendation models," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4083-4119, May.
    7. Lea Helmers & Franziska Horn & Franziska Biegler & Tim Oppermann & Klaus-Robert Müller, 2019. "Automating the search for a patent’s prior art with a full text similarity search," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-17, March.
    8. Ana Teresa Santos & Sandro Mendonça, 2022. "Do papers (really) match journals’ “aims and scope”? A computational assessment of innovation studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7449-7470, December.

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