Content Distribution System as a Component of the Development of Social Mechanics in a Financial Application

This master thesis is devoted to the development of high-loaded services in the field of automating the process of aggregation of data from various sources. In the modern world the amount of data and news sources are reaching critical levels. A huge amount of new content including news sites, blogs, and social media is being produced every day. This volume of data significantly increases the level of information noise and makes it almost impossible to find relevant and interesting information, so users are choosing to use centralized news aggregations rather than individual news sources. In this regard, the popularity of personalized content aggregation systems based on machine learning algorithms is growing. The developed approach can be used as a basic model for developing personalized content aggregation tools using not only standard ranking models, but also, for example, social graphs, and has a wide potential for expansion and refinement (for example, providing commercial information as well as news feeds data). The paper contains 32 pages, 3 chapters, 9 figures, 10 listings, 24 sources. Keywords: content distribution, high-load systems, automated data collection.