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Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

Author

Listed:
  • Krzysztof Przystupa

    (Department of Automation, Lublin University of Technology, 20-618 Lublin, Poland)

  • Mykola Beshley

    (Department of Telecommunications, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Olena Hordiichuk-Bublivska

    (Department of Telecommunications, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Marian Kyryk

    (Department of Telecommunications, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Halyna Beshley

    (Department of Telecommunications, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Julia Pyrih

    (Department of Telecommunications, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Jarosław Selech

    (Institute of Machines and Motor Vehicles, Poznan University of Technology, 60-965 Poznan, Poland)

Abstract

The problem of analyzing a big amount of user data to determine their preferences and, based on these data, to provide recommendations on new products is important. Depending on the correctness and timeliness of the recommendations, significant profits or losses can be obtained. The task of analyzing data on users of services of companies is carried out in special recommendation systems. However, with a large number of users, the data for processing become very big, which causes complexity in the work of recommendation systems. For efficient data analysis in commercial systems, the Singular Value Decomposition (SVD) method can perform intelligent analysis of information. With a large amount of processed information we proposed to use distributed systems. This approach allows reducing time of data processing and recommendations to users. For the experimental study, we implemented the distributed SVD method using Message Passing Interface, Hadoop and Spark technologies and obtained the results of reducing the time of data processing when using distributed systems compared to non-distributed ones.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2284-:d:538791
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    References listed on IDEAS

    as
    1. Guangli Li & Jin Hua & Tian Yuan & Jinpeng Wu & Ziliang Jiang & Hongbin Zhang & Tao Li, 2019. "Novel Recommendation System for Tourist Spots Based on Hierarchical Sampling Statistics and SVD++," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-15, July.
    2. 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.
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