Recommendation systems for logistics companies
Rubrics: SCIENTIFIC ARTICLES
Authors:
1.
Siberian State University of Science and Technology named after Academician M.F. Reshetnev
student
student
2.
ANO SRP "Siberian Academy of Beekeeping"
(Head of the Department of IES, Scientific supervisor)
employee
employee
Type:
Article
Pages:
from 213
to 216
Status:
Published
Received:
09.12.2024
Accepted:
11.12.2024
Published:
11.12.2024
Subject area:
UDK 004.4 Программные средства
Language:
Russian
Keywords:
recommendation system, logistics, data analysis, content filtering
Abstract (English):
. This article discusses the development of a recommendation system for the Siberian Logistics Company (SLC) aimed at optimizing the supply of food products to retail points. In the face of increasing competition, companies are forced to improve service quality and offer products that meet the needs of retail locations. The recommendation system, based on content filtering methods, analyzes real-time data on demand, assortment, and stock levels, providing personalized recommendations for sales representatives. The article describes the technical features of the system, the selection of tools and approaches, as well as the use of Python and Jupyter Notebook for development. The implementation of the system significantly accelerates data processing, improves the accuracy of recommendations, and enhances the company's adaptation to market changes. In the future, the system’s functionality can be expanded with the use of artificial intelligence and predictive analytics.
. This article discusses the development of a recommendation system for the Siberian Logistics Company (SLC) aimed at optimizing the supply of food products to retail points. In the face of increasing competition, companies are forced to improve service quality and offer products that meet the needs of retail locations. The recommendation system, based on content filtering methods, analyzes real-time data on demand, assortment, and stock levels, providing personalized recommendations for sales representatives. The article describes the technical features of the system, the selection of tools and approaches, as well as the use of Python and Jupyter Notebook for development. The implementation of the system significantly accelerates data processing, improves the accuracy of recommendations, and enhances the company's adaptation to market changes. In the future, the system’s functionality can be expanded with the use of artificial intelligence and predictive analytics.
Keywords:
recommendation system, logistics, data analysis, content filtering
recommendation system, logistics, data analysis, content filtering
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