Classifications of users on online platforms using machine learning techniques
DOI:
https://doi.org/10.17721/1812-5409.2022/4.8Keywords:
user classification, user analysis, сluster analysis, association rules, neural networksAbstract
Online platforms have become an integral part of our lives, and the number of users is increasing by the day. From social media platforms to e-commerce websites, these platforms are used by millions of people around the world. With such a large user base, it is essential for these platforms to classify their users based on their behavior, preferences, and interests. This paper explores how machine learning can be used to classify users on online platforms.
When classifying users, they are divided into different categories based on their characteristics. By analyzing user behavior and preferences, online platforms can personalize their services and provide a better user experience. Machine learning techniques can help online platforms automate the classification process and reduce human effort. In this article, the behavioral classification of users on online platforms will be discussed in detail.
Pages of the article in the issue: 66 - 71
Language of the article: Ukrainian
References
Driver and Kroeber (1932). "Quantitative Expression of Cultural Relationships". University of California Publications in American Archaeology and Ethnology. Berkeley, CA: University of California Press. Quantitative Expression of Cultural Relationships: – P. 211–256.
Zubin, Joseph (1938). "A technique for measuring like-mindedness". The Journal of Abnormal and Social Psychology. – P. 508–516.
Tryon, Robert C. (1939). Cluster Analysis: Correlation Profile and Orthometric (factor) Analysis for the Isolation of Unities in Mind and Personality. Edwards Brothers.
Cattell, R. B. (1943). "The description of personality: Basic traits resolved into clusters". Journal of Abnormal and Social Psychology. – P. 476–506.
Piatetsky-Shapiro, Gregory (1991), Discovery, analysis, and presentation of strong rules, in Piatetsky-Shapiro, Gregory; and Frawley, William J.; eds., Knowledge Discovery in Databases, AAAI/MIT Press, Cambridge, MA.
Agrawal, R.; Imieliński, T.; Swami, A. (1993). "Mining association rules between sets of items in large databases". Proceedings of the 1993 ACM SIGMOD international conference on Management of data - SIGMOD '93. – P. 207.
Garcia, Enrique (2007). "Drawbacks and solutions of applying association rule mining in learning management systems" (PDF). Sci2s. Archived (PDF) from the original.
Hopfield, J. J. (1982). "Neural networks and physical systems with emergent collective computational abilities". Proc. Natl. Acad. Sci. U.S.A. – P. 2554–2558.
"Neural Net or Neural Network – Gartner IT Glossary". www.gartner.com.
Travis Oliphant (2007). "Python for Scientific Computing" (PDF). Computing in Science and Engineering.
Charles R Harris; K. Jarrod Millman; Stéfan J. van der Walt; et al. (2020). "Array programming with NumPy" (PDF). // 585 (7825): 357–362.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 The Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
