Peer-reviewed articles 17,970 +



Title: THE COMPARISON OF CLASSIFICATION ALGORITHMS OF USERS TO PERSONALIZE THE DATA PROVIDED TO THE USER

THE COMPARISON OF CLASSIFICATION ALGORITHMS OF USERS TO PERSONALIZE THE DATA PROVIDED TO THE USER
N. D. Matrosova;D. G. Shtennikov;M. S. Ibragimova
1314-2704
English
19
2.1
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.
conference
19th International Multidisciplinary Scientific GeoConference SGEM 2019
19th International Multidisciplinary Scientific GeoConference SGEM 2019, 30 June - 6 July, 2019
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference-SGEM
Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
653-658
30 June - 6 July, 2019
website
cdrom
5407
classification; machine learning; personalization; interface