МАШИННОЕ ОБУЧЕНИЕ МОДЕЛИ ИНФОРМАЦИОННОЙ РЕКОМЕНДАТЕЛЬНОЙ СИСТЕМЫ ПО ВОПРОСАМ ИНДИВИДУАЛИЗАЦИИ ОБРАЗОВАНИЯ

Вестник московского университета им Витте. 2019. Т. 2. № 27. С. 7–14.

Taratuhina Y., Барт Т. В., Власов В. В.

Training model information recommendation system is associated with the study of applied mathematical and
information methods and models, their combinations in order to ensure the necessary accuracy of the forecasts
and conclusions. The article deals machine learning of model recommendation system using statistical methods
and analysis of big data, aimed at addressing the issues of individualization of education. In this case, the accuracy
of the machine learning model depends on the type of statistical model used to predict the probability of
some event from the values of the set of features, as well as the training sample used to select the parameters,
and the regularization function used to improve the generalizing ability of the resulting model. The study tested
models based on logistic regression, methods of naive Bayesian classifier (Naïve Bayes), lasso-type regression.
Experimentally confirmed the theoretical assumption about the possibility of creating a recommendation system
on the individualization of education on the basis of an array of educational data, including the results of educational
and extracurricular activities of students. Conclusions about the presence of correlation dependencies in
the data, which can be used to improve the accuracy of the model of the recommendation system, are formulated.

Keywords: рекомендательная система, анализ больших данных, Big Data analysis