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COMPARATIVE ANALYSIS OF CONTENT FILTERING ALGORITHMS IN SOCIAL NETWORKS

Nekrasov Nikita Mikhailovich  (PhD student, Financial University under the Government of the Russian Federation, Moscow, Russia)

In this article, the author conducted a review of content filtering algorithms in social networks, such as TF-IDF and Naive Bayes. Each algorithm is examined in the context of its advantages, disadvantages, and potential areas for improvement. The presented comparative analysis demonstrates which algorithm is better suited for content filtering in social networks, by comparing them based on a specific example of classifying comments on a post in the social network VKontakte.

Keywords:TF-IDF, Naive Bayes, content filtering, social networks
 

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Citation link:
Nekrasov N. M. COMPARATIVE ANALYSIS OF CONTENT FILTERING ALGORITHMS IN SOCIAL NETWORKS // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2024. -№10. -С. 120-123 DOI 10.37882/2223-2966.2024.10.31
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