Keywords
recommendation algorithm
esports
internet media
video content
How to Cite
Abstract
Introduction. The purpose of the study is to identify relevant effective strategies for interaction with the recommendation algorithm on the YouTube platform in the process of creating and distributing video content. Since YouTube is one of the most popular Internet services and without a doubt number one video hosting in the world. YouTube recommendation system is very important part of content distributing, that’s why its important to research this topic. Relevance of the study. The topic of the study is relevant because YouTube is an active global platform for the distribution of video content. YouTube is the most visited video viewing platform in Ukraine and one of the most visited websites in the world. Understanding the work of recommendation algorithms will allow you to adapt video content so that it reaches the widest possible target audience. Methodology. Methods of interpretation and structural analysis were used during the process of researching this topic. Results. YouTube Analytics opens eyes to key performance indicators of videos: relevance and novelty, attractiveness (clickability) of thumbnail images, frequency of channel downloads, viewers ‘reaction to previous videos, viewers’ returns, audience retention, etc. Proper and systematic work with the audience, improving the quality of content by analyzing the rankings of previous publications will help retain the existing audience and effectively find a new one. Conclusions. This study can serve as a basis for further research into YouTube’s recommendation algorithms. The article presents an interpretation of only the main parameters of the video that affect the recommendation rating, but future research can either explore more deeply the already covered parameters, or pay attention to others.
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