DEVELOPMENT OF PERSONALIZATION SYSTEM OF SPECIALIZED WEB PORTAL

N. G. Axak

Abstract


Context. The actual task of personifying a Web portal providing business services (telemedicine, consultations, remote monitoring,
distance education, etc.) has been solved.
Objective - development of a personalization system for a web portal that provides specialized services, which allows to take into account
preferences of users for the improvement of quality of service, an acceleration of information search, an exception of uninteresting pages, and а customer retention.
Method. The generalized process personalization model of Internet service is offered. The method of adaptation of the Web-resource
based on the combination of agent and neural network technologies is proposed in a model which automatically generates content for certain
categories of Internet users. The document object model of site in a graph form to search of relevant information was proposed that allows the site personalization. The use of multi-agent structure allowed to realize interaction of the components of the developed model. The method includes the following actions: automatic generation of hypotheses, which determines the presence or absence of target properties of the user; analysis of the user’s behavior on his surfing the Internet that allows to give more relevant results; construction of information portrait for collection statistically significant set of information characteristics for the purpose of planning of further actions; parallel clustering of users with use of the self-organizing Kohonen maps for the purpose of an acceleration of processing big data. The self-organizing Kohonen maps are adapted to symmetric multiprocessing system for accelerating computations. Thus, the configuration of the computing system shall be a multiple of the dimension of the input data for reduction of computation time.
Results. For the proposed models and method, software and a web interface are developed. They are used to realization computing
experiments to verification of the models, valuation of the adequacy and study the properties of the model and method.
Conclusions. The conducted experiments have confirmed the proposed models and methods. The use of a set of methods and tools can
be used in practice to promote goods and services in the network, to provide various services or individual parts of it, for business development.

Keywords


personalization; JSM-method; neural network clustering; multi-agent system; informative portrait of user.

References


Xiao J., Zhang Y. Clustering of web users using session-based

similarity measures, Computer Networks and Mobile Computing,

Proceedings. 2001 International Conference on. IEEE,

, pp. 223-228. DOI: 10.1109/ICCNMC.2001.962600.

Chen L., Bhowmick S. S., Nejdl W. COWES: Web user clustering

based on evolutionary web sessions, Data & Knowledge

Engineering, 2009, Vol. 68, No. 10, pp. 867–885. DOI: 10.1016/

j.datak.2009.05.002.

Selvakumar K., Ramesh L. S., Kannan A. Enhanced K-Means

Clustering Algorithm for Evolving User Groups, Indian Journal

of Science and Technology, 2015, Vol. 8, No. 24, P. 1. DOI:

17485/ijst/2015/v8i24/80192.

Ganesan S., Sivaneri A. I. U., Selvaraju S. K. Evolving interest

based user groups using PSO algorithm, Recent Trends in

Information Technology (ICRTIT), 2014 International Conference

on, IEEE, 2014, pp. 1–6. DOI: 10.1109/ICRTIT.2014.6996196.

Andreeva K. A., Shajdurov R. S., Morgunov E. P. Primenenie

nejronnoj seti Kohonena dlja klassifikacii web-stranic

informacionno-poiskovoj sistemoj sajtov, Aktual’nye problemy

aviacii i kosmonavtiki, 2015, Vol. 1, No. 11, pp. 380–381.

Zerhari B., Lahcen A. A., Mouline S. Big data clustering: Algorithms

and challenge, Proc. of Int. Conf. on Big Data, Cloud and

Applications (BDCA’15), 2015.

Kurasova O. et al. Strategies for big data clustering, 2014 IEEE

th International Conference on Tools with Artificial Intelligence,

IEEE, 2014, pp. 740–747.

Axak N. Development of multi-agent system of neural network

diagnostics and remote monitoring of patient, Eastern-European

Journal of Enterprise Technologies, 2016, 4/9 (82), pp. 4–11.

Axak N., Korgut S., Komoda P. Decision support system for

intelligent site, Elektronika (LIV), No. 8/2013, pp. 52–59.

Anshakov O. M. DSM-metod: teoretiko-mnozhestvennoe

ob#jasnenie, NTI. Ser. 2. 2012, № 9.

Finn V. K. Induktivnye metody D.S. Millja v sistemah

iskusstvennogo intellekta. Chast’ I, Iskusstvennyj intellekt i

prinjatie reshenij, 2010, No. 3, pp. 3–21.

Shklovets A. V., Axak N. G. Visualization of high-dimensional

data using two-dimensional self-organizing piecewise-smooth

Kohonen maps, Optical Memory and Neural Networks, 2012,

Vol. 21, No. 4, pp. 227–232. DOI: 10.3103/

S1060992X12040066.


GOST Style Citations


1. Xiao J. Clustering of web users using session-based similarity measures/ J. Xiao, Y. Zhang // Computer Networks and Mobile Computing, 2001. Proceedings. 2001 International Conference on. – IEEE, 2001. – P. 223–228. DOI: 10.1109/
ICCNMC.2001.962600
2. Chen L. COWES: Web user clustering based on evolutionary web sessions / L. Chen, S. S. Bhowmick, W. Nejdl // Data & Knowledge Engineering. – 2009. – Vol. 68, No. 10. – P. 867–885. DOI:10.1016/j.datak.2009.05.002 ·
3. Selvakumar K. Enhanced K-Means Clustering Algorithm for
Evolving User Groups / K. Selvakumar, L. S. Ramesh, A. Kannan //Indian Journal of Science and Technology. – 2015. – Vol. 8, No.24. – P. 1. DOI: 10.17485/ijst/2015/v8i24/80192
4. Ganesan S. Evolving interest based user groups using PSO algorithm / S. Ganesan, A. I. U. Sivaneri, S. K. Selvaraju // Recent Trends in Information Technology (ICRTIT), 2014 International Conference on. – IEEE, 2014. – P. 1–6. DOI: 10.1109/ICRTIT.2014.6996196
5. Андреева К. А. Применение нейронной сети Кохонена для классификации web-страниц информационно-поисковой системой сайтов / К. А. Андреева, Р. С. Шайдуров, Е. П. Моргунов // Актуальные проблемы авиации и космонавтики. – 2015. – Т. 1, № 11– C. 380–381.
6. Zerhari B. ’Big data clustering: Algorithms and challenge’ / B. Zerhari, A. A. Lahcen, S. Mouline // Proc. of Int. Conf. on Big Data, Cloud and Applications (BDCA’15). – 2015.
7. Kurasova O. Strategies for big data clustering / O. Kurasova et al. // 2014 IEEE 26th International Conference on Tools with
Artificial Intelligence. – IEEE, 2014. – С. 740–747. DOI: 10.1109/ICTAI.2014.115
8. Axak N. Development of multi-agent system of neural network diagnostics and remote monitoring of patient / N. Axak // Eastern-European Journal of Enterprise Technologies. – 2016. – 4/9 (82) – P. 4–11. DOI: http://dx.doi.org/10.15587/1729-4061.2016.75690
9. Axak N. Decision support system for intelligent site / N. Axak, S. Korgut, P. Komoda //Elektronika (LIV). – 2013. – No. 8. – P. 52–59.
10. Аншаков О. М. ДСМ-метод: теоретико-множественное объяснение / О. М. Аншаков // НТИ. Сер. 2. – 2012. – № 9.
11. Финн В. К. Индуктивные методы Д. С. Милля в системах искусственного интеллекта. Часть I / В. К. Финн // Искусственный интеллект и принятие решений. – 2010. – № 3. – С. 3–21.
12. Shklovets A. V. Visualization of high-dimensional data using twodimensional self-organizing piecewise-smooth Kohonen maps / A. V. Shklovets , N. G. Axak // Optical Memory and Neural Networks. – 2012. – Vol. 21, No. 4. – P. 227–232. DOI: 10.3103/S1060992X12040066.




DOI: https://doi.org/10.15588/1607-3274-2018-1-11



Copyright (c) 2018 N. G. Axak

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Address of the journal editorial office:
Editorial office of the journal «Radio Electronics, Computer Science, Control»,
Zaporizhzhya National Technical University, 
Zhukovskiy street, 64, Zaporizhzhya, 69063, Ukraine. 
Telephone: +38-061-769-82-96 – the Editing and Publishing Department.
E-mail: rvv@zntu.edu.ua

The reference to the journal is obligatory in the cases of complete or partial use of its materials.