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Text-Based Product Matching with Incomplete and Inconsistent Items Descriptions

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12743))

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Abstract

In recent years Machine Learning and Artificial Intelligence are reshaping the landscape of e-commerce and retail. Using advanced analytics, behavioral modeling, and inference, representatives of these industries can leverage collected data and increase their market performance. To perform assortment optimization – one of the most fundamentals problems in retail – one has to identify products that are present in the competitors’ portfolios. It is not possible without effective product matching. The paper deals with finding identical products in the offer of different retailers. The task is performed using a text-mining approach, assuming that the data may contain incomplete information. Besides the description of the algorithm, the results for real-world data fetched from the offers of two consumer electronics retailers are being demonstrated.

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Acknowledgment

The work was supported by the Faculty of Physics and Applied Computer Science AGH UST statutory tasks within the subsidy of MEiN.

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Authors and Affiliations

  1. Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059, Kraków, Poland

    Szymon Łukasik & Piotr A. Kowalski

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Szymon Łukasik & Piotr A. Kowalski

  3. Synerise PLC, ul. Lubostroń 1, 30-383, Kraków, Poland

    Szymon Łukasik & Andrzej Michałowski

  4. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia

    Amir H. Gandomi

Authors
  1. Szymon Łukasik
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  2. Andrzej Michałowski
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  3. Piotr A. Kowalski
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  4. Amir H. Gandomi
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Corresponding author

Correspondence to Szymon Łukasik .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Łukasik, S., Michałowski, A., Kowalski, P.A., Gandomi, A.H. (2021). Text-Based Product Matching with Incomplete and Inconsistent Items Descriptions. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12743. Springer, Cham. https://doi.org/10.1007/978-3-030-77964-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-77964-1_8

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