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News Feeds Based on Machine Learning as a Platform for Content Distribution in Russia

Научные исследования: 



For citation: Pershina E.D. (2019) Novostnye lenty na osnove mashinnogo obucheniya kak ploshchadki dlya distributsii kontenta v Rossii [News Feeds Based on Machine Learning as a Platform for Content Distribution in Russia]. Mediaskop 2. (in Russian). Available at:

DOI: 10.30547/mediascope.2.2019.6


© Elena D. Pershina

PhD Student at the Chair of Media Theory and Economics, Faculty of Journalism, Lomonosov Moscow State University, Manager at Yandex (Moscow, Russia),



The purpose of the study is to examine the current situation with machine learning generated news feeds in the Russian market. The trend of news consumption through this type of feeds stands out as one of the main trends in KPCB 2017 international research. In addition, we have tried to analyze how the content of the largest Runet sources is presented in news feeds. In the course of our research, we have revealed several features of such feeds: the main emphasis in the distribution of feeds is made on the platform of web browsers of parent companies, the content is not purely news, and the feeds are consumed by users not only on mobile devices. The content of most large content sites in Runet is presented in the feed of the market leader – Yandex Zen. The presence of UGC is not an obstacle to use this platform to attract additional traffic to the resource.

Keywords: business model, machine feeds, zen, news feed, content distribution.



Lamburt V., Trabun D., Solomentsev D., Bakunov G. «Sekretnyy doklad». Konferentsiya YAC, may 2017 ['Secret Report'. YAC Conference, May 2017]. Available at:

Meeker M. (2017) Internet Trends 2017 − Сode conference. May 31. Available at: (aссessed: 30.01.2018).

TNS Web Index Desktop, ноябрь 2017, Россия 0+. Available at:

Tokurov T. Osobennosti trafika iz «Yandeks.Dzena» i rekomendatsiy v Google Chrome [Traffic Features from 'Yandex.Zen' and Recommendations by Google Chrome]. Available at:



Asmus A.V. (2009) Novye mediaplatformy [New Mediaplatforms]. Mediaskop 2. (in Russian). Available at:новые-медиаплатформы

Guy I., Ronen I., Raviv A. (2011) Personalized Activity Streams: Sifting Through the "River of News". Proceedings of the fifth ACM conference on Recommender systems. ACM, pp. 181−188.

Karyakina K.A. (2010) Aktual'nye formy i modeli novykh media: ot ponimaniya auditorii k sozdaniyu kontenta [Topical Forms and Models of New Media: from Audience Comprehension to Content Creation]. Mediaskop 1. (in Russian). Available at:актуальные-формы-и-модели-новых-медиа-от-понимания-аудитории-к-созданию-контента

Kruglova L.A.(2016) Rossiyskie pechatnye SMI: spetsifika onlayn-versiy [Russian Print Media: Specificity of Online Versions]. Mediaskop 4. (in Russian). Available at:

Perell D. (2016) Models of Internet Monetization. Elon Journal of Undergraduate Research in Communications 7 (1). Available at:

Pershina E.D. (2017) Printsipy vybora rossiyskimi novostnymi media ploshchadok dlya sozdaniya grupp v sotsial'nykh setyakh [Russian News Media Principles in Selection of Platforms for Creating Groups on Social Networks]. Mediaskop 2. (in Russian). Available at:

Zamkov A.V., Krasheninnikova M.A., Lukina M.M., Tsynareva N.A. (2017) Robotizirovannaya zhurnalistika: ot nauchnogo diskursa k zhurnalistskomu obrazovaniyu [Robotic Journalism: from Scientific Discourse to Journalism Education]. Mediaskop 2. (in Russian). Available at: