Journal article
Michał Malinowski
Management and Business Research Quarterly, vol. 19, 2021, pp. 14-32
Management and Business Research Quarterly, vol. 19, 2021, pp. 14-32
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APA
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Malinowski, M. (2021). Implementation of Recommendation Algorithm based on Recommendation Sessions in E-commerce {IT} System. Management and Business Research Quarterly, 19, 14–32. https://doi.org/10.32038/mbrq.2021.19.02
Chicago/Turabian
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Malinowski, Michał. “Implementation of Recommendation Algorithm Based on Recommendation Sessions in E-Commerce {IT} System.” Management and Business Research Quarterly 19 (2021): 14–32.
MLA
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Malinowski, Michał. “Implementation of Recommendation Algorithm Based on Recommendation Sessions in E-Commerce {IT} System.” Management and Business Research Quarterly, vol. 19, 2021, pp. 14–32, doi:10.32038/mbrq.2021.19.02.
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@article{micha2021a,
title = {Implementation of Recommendation Algorithm based on Recommendation Sessions in E-commerce {IT} System},
year = {2021},
journal = {Management and Business Research Quarterly},
pages = {14-32},
volume = {19},
doi = {10.32038/mbrq.2021.19.02},
author = {Malinowski, Michał}
}
This paper presents a study on the implementation of the author's Algorithm of Recommendation Sessions (ARS) in an operational e-commerce information system and analyses the basic parameters of the resulting recommendation system. It begins with a synthetic overview of recommendation systems, followed by a presentation of the proprietary ARS algorithm, which is based on recommendation sessions. A mathematical model of the recommendation session, constructed using graph and network theory, serves as the input for the ARS algorithm. This paper also explores graph structure representation methods and the implementation of a G graph (representing a set of recommendation sessions) in a relational database using the SQL standard. The ARS algorithm was implemented in a working e-commerce information system, leading to the development of a fully functional recommendation system adaptable to various e-commerce IT systems. The effectiveness of the algorithm is demonstrated by research on the recommendation system's parameters presented in the final section of the paper.
