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THE COMPARISON OF CLASSIFICATION ALGORITHMS OF USERS TO PERSONALIZE THE DATA PROVIDED TO THE USER
Abstract
The information system interface consists of various elements. Attention of the user decreases when working with a large amount of information. The authors suggest that it is possible to implement recommendatory (adaptive) modules for users, using changes in the graphical part of the interface. Such changes are called interface personalization. Personalization allows you to focus the user's attention on the most relevant elements of the user. To reduce the resource consumption of recommendation modules, it makes sense to create them not for each individual user, but for groups. This will help the classification. Classification is the process of ordering or distributing objects (observations) into classes in order to reflect the relations between them. The study will be conducted using a set of data obtained from one of the massive open online courses. Using data from MOOC-systems has a large number of signs by which classification can be carried out. Further results of the research can be extrapolated to other information systems. Therefore, the authors decided to compare the user classification algorithms to personalize the information provided to the user.
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