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Predicting the Quality of Zen Authors' Publications

Student: Romanov Iaroslav

Supervisor: Alexey Masyutin

Faculty: Faculty of Computer Science

Educational Programme: Financial Technology and Data Analysis (Master)

Year of Graduation: 2020

Major social services are interested in offering users high-quality content. Quality may mean different things depending on the specifics of the service, but what is common to different social platforms is the need to classify the available content in order to select and promote the best. The Yandex Zen service reviewed in this paper has a significant amount of material, but only a small part of it is classified. Therefore, in the presented work, the problem of concentration of high-quality material in a subset of all publications was set and solved. The problem was set as a binary classification problem with the possibility of obtaining the probability of a class. In the theoretical part of the work, an analysis of approaches to solving similar problems was carried out. In the practical part, the available data was analyzed, the necessary preprocessing was performed, models of classical machine learning and neural networks were tested to find the best solution, and metrics of algorithms for solving the problem were compared. In conclusion, the results of the work are summarized, relevant conclusions are drawn and ways of further improvement are identified.

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