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

Krzysztof Przystupa, Mykola Beshley, Olena Hordiichuk-Bublivska, Marian Kyryk, Halyna Beshley, Julia Pyrih and Jarosław Selech
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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

Energies, 2021, vol. 14, issue 8, 1-24

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.

Keywords: big data (BD); message passing interface (MPI); distributed systems (DS); singular value decomposition (SVD); hadoop; spark (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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