https://libeldoc.bsuir.by/handle/123456789/32556
Title: | Unsupervised ranking of clients: machine learning approach to define a "good customer" |
Authors: | Parkhimenka, U. Tatur, M. Khandogina, O. |
Keywords: | публикации ученых;loyalty ladder;ranking;ecommerce;automatic marketing decision-making;machine learning;data mining & knowledge discovery;latent variable analysis |
Issue Date: | 2017 |
Publisher: | Faculty of Management Science and Informatics |
Citation: | Parkhimenka, U. Unsupervised ranking of clients: machine learning approach to define a "good customer" / U. Parkhimenka, M. Tatur, O. Khandogina // Central European Researchers Journal. – 2017. - Volume 3, Issue 2. - Pp. 10 - 15. |
Abstract: | Ranking of clientsis a natural problem for every business. Though usually it can be solved by common sense and intuition of managers, in the case of a big business entity (e.g. global online stores), the problem becomes more complicated with obvious obstacles in derivation of fast and accurate solution. This article deals with the clients ranking problem using machine learning methodology. |
URI: | https://libeldoc.bsuir.by/handle/123456789/32556 |
Appears in Collections: | Публикации в зарубежных изданиях |
File | Description | Size | Format | |
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Parkhimenka_Unsupervised.pdf | 488.88 kB | Adobe PDF | View/Open |
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