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European Conference on Information Retrieval

ECIR 2022: Advances in Information Retrieval pp 316–320Cite as

SolutionTailor: Scientific Paper Recommendation Based on Fine-Grained Abstract Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13186))

Abstract

Locating specific scientific content from a large corpora is crucial to researchers. This paper presents SolutionTailor (The demo video is available at: https://mm.doshisha.ac.jp/sci2/SolutionTailor.html), a novel system that recommends papers that provide diverse solutions for a specific research objective. The proposed system does not require any prior information from a user; it only requires the user to specify the target research field and enter a research abstract representing the user’s interests. Our approach uses a neural language model to divide abstract sentences into “Background/Objective” and “Methodologies” and defines a new similarity measure between papers. Our current experiments indicate that the proposed system can recommend literature in a specific objective beyond a query paper’s citations compared with a baseline system.

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Notes

  1. 1.

    https://scholar.google.co.jp/citations?view_op=top_venues.

  2. 2.

    We evaluated not the final similarity score but only \(cos_{BO}\) because the competition papers do not always have significantly different solutions.

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Acknowledgment

This research was partly supported by JSPS KAKENHI Grant Number 20H04484 and JST ACT-X grant number JPMJAX1909.

Author information

Authors and Affiliations

  1. Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto, Japan

    Tetsuya Takahashi & Marie Katsurai

Authors
  1. Tetsuya Takahashi
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  2. Marie Katsurai
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Corresponding authors

Correspondence to Tetsuya Takahashi or Marie Katsurai .

Editor information

Editors and Affiliations

  1. Martin Luther University Halle-Wittenberg, Halle, Germany

    Matthias Hagen

  2. Leiden University, Leiden, The Netherlands

    Suzan Verberne

  3. University of Glasgow, Glasgow, UK

    Craig Macdonald

  4. University of Duisburg-Essen, Essen, Germany

    Christin Seifert

  5. University of Stavanger, Stavanger, Norway

    Krisztian Balog

  6. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  7. University of Stavanger, Stavanger, Norway

    Vinay Setty

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Takahashi, T., Katsurai, M. (2022). SolutionTailor: Scientific Paper Recommendation Based on Fine-Grained Abstract Analysis. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_40

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  • DOI: https://doi.org/10.1007/978-3-030-99739-7_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-99738-0

  • Online ISBN: 978-3-030-99739-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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