Влияние эффекта масштаба рекомендательных систем на конкуренцию в секторах цифровых платформ

Проблемы прогнозирования. 2025. № 3. С. 135–145.

Avdasheva S. B., Khomik O., Chesnokov V., Хлюпина В. А.

Over the past quarter-century, digital platforms proliferated and became the world’s
most valuable companies. Traditionally, the growth of digital platforms is explained by cross-
platform network effects, which, in turn, are supported by recommendation systems – a set of
algorithms that suggest the most suitable user of one type to a user of another type. The depend-
ence of the accuracy ensured by algorithm predictions on the number of observation units and on
the number and type of observations for each unit—returns to scale—affects the comparative
competitiveness of large and small platforms, the structure of markets, and hence the choice of
public policy instruments in relation to the platforms. The aim of the article is to systematize the
data regarding the returns to scale of recommendation systems on digital platforms. The results
obtained by empirical studies and the analysis of coverage and convergence indicators for some
Russian platforms cast doubt on the significant positive return of recommendation system accu-
racy with regard to the number of users: it largely depends on the designed set of algorithms.
Improvement in recommendation system algorithms will make it possible for even smaller Rus-
sian platforms to remain competitive with a limited number of users.

Keywords: конкурентная политика, рекомендательные системы, эффект масштаба, цифровые платформы

Publication based on the results of: Restructuring of the Russian Companies, Industries and Markets in Response to New Global Challenges (2025)