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Graph-Based Recommendation Engine for Stock Investment Decisions

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Web Information Systems and Technologies (WEBIST 2020, WEBIST 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 469))

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Abstract

Real-time recommender systems face the challenge of fast-changing data and the necessity of providing the answers in almost no time. In this paper, we discuss the case of a recommendation system for the stock market that uses knowledge about investors similar to the target user and combines it with a technical analysis of the stocks.

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Notes

  1. 1.

    NASDAQ is a first online stock market based in New York. See https://www.nasdaq.com/.

  2. 2.

    See: https://neo4j.com/.

  3. 3.

    Link: https://github.com/R-tooR/recommendation-system/tree/master.

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Author information

Authors and Affiliations

  1. AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059, Krakow, Poland

    Artur Bugaj & Weronika T. Adrian

Authors
  1. Artur Bugaj
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  2. Weronika T. Adrian
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Corresponding author

Correspondence to Weronika T. Adrian .

Editor information

Editors and Affiliations

  1. University of Padua, Padua, Italy

    Massimo Marchiori

  2. University of Seville, Seville, Spain

    Francisco José Domínguez Mayo

  3. Polytechnic Institute of Setúbal/INSTICC, Setubal, Portugal

    Joaquim Filipe

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Bugaj, A., Adrian, W.T. (2023). Graph-Based Recommendation Engine for Stock Investment Decisions. In: Marchiori, M., Domínguez Mayo, F.J., Filipe, J. (eds) Web Information Systems and Technologies. WEBIST WEBIST 2020 2021. Lecture Notes in Business Information Processing, vol 469. Springer, Cham. https://doi.org/10.1007/978-3-031-24197-0_8

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  • DOI: https://doi.org/10.1007/978-3-031-24197-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24196-3

  • Online ISBN: 978-3-031-24197-0

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