IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0158423.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0158423
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0158423&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0158423?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0158423. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.