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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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García-Barrios V.M., Mödritscher F., Gütl C. Personalisation versus Adaptation? A User-centred Model Approach and its Application. 2019. ResearchGate; [accessed 2019 May 03] https://www.researchgate.net/publication/228352085_Personalisation_versus_Adaptation_A_User-centred_Model_Approach_and_its_Application
Mulvenna M.D., Anand S.S., Büchner A.G. Personalization on the Net using Web mining: introduction. Communications of the ACM. 2000. Vol. 43. No 8. P. 122– 125.
Esmaeili L., Nasiri M., Minaei-Bidgoli B. (2011) Personalizing Group Recommendation to Social Network Users. In: Gong Z., Luo X., Chen J., Lei J., Wang F.L. (eds) Web Information Systems and Mining. WISM 2011. Lecture Notes in Computer Science, vol 6987. Springer, Berlin, Heidelberg DOI: 10.1007/978-3-642-23971-7_18
Y. Elyusufi, H. Seghiouer and M. A. Alimam, "Building profiles based on ontology for recommendation custom interfaces," 2014 International Conference on Multimedia Computing and Systems (ICMCS), Marrakech, 2014, pp. 558-562. DOI: 10.1109/ICMCS.2014.6911166
P.G., Om Prakash & Jaya, A. (2018). Analyzing and predicting user navigation pattern from weblogs using modified classification algorithm. Indonesian Journal of Electrical Engineering and Computer Science. 11. 333-340. DOI: 10.11591/ijeecs.v11.i1.pp333-340.
Lineynyy koeffitsient korrelyatsii r-pirsona [R-Pearson linear correlation coefficient]. Matematicheskaya statistika dlya psikhologov [Mathematical statistics for psychologists]. [accessed 2019 April 03] https://statpsy.ru/pearson/linear-pirson/
Jain А. K. Data clustering: A review / A. K. Jain, M. N. Murty, P. J. Flynn. ACM Computing Surveys. 1999. Vol. 31, No. 3. P. 264–323.
Matrosova N.D., Shtennikov D.G. The personification of the user’s interface: classification vs. clusterization of users of online courses. Environment.Technology.Resources. 2019. Proceedings of the 12th International Scientific and Practical Conference; [accessed 2019 May 01] https://conferences.ru.lv/index.php/ETR/ETR2019/paper/view/3100
sklearn.metrics.precision_recall_fscore_support. c2007-2019. sckit-learn; [accessed 2019 April 20]. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html
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