FEATURES OF THE ARCHITECTURE FOR INTERNET COMMERCIAL CONTENT MANAGEMENT SYSTEM BASED ON METHODS OF MACHINE LEARNING, WEB MINING AND SEO TECHNOLOGIES
Keywords:Сommercial content, personalization, Web mining, Machine Learning, SEO technology, metrics search, ecommerce, NLP, content monitoring, content analysis, statistical linguistic analysis, quantitative linguistics.
AbstractContext. Today, most corporations are constantly rethinking business from the point of view of the Internet, namely its
availability, broad reach and ever-changing needs of the user. The e-commerce web-site, which provides user-friendly experience,
including the ability to quickly find the products that are necessary for its portables and taste, is more in favor of competitive
Objective of the study is to develop a general architecture of the intellectual system for the distribution of commercial content in
the Internet space, based on the study of the neural network in accordance with the history of the psychedelic region to provide
unique content using the approach of personalization and the use of tags.
Method. The model of information system of commercial content personalization for the user needs is developed. Also the
method of distributing commercial content based on the approach of personalization and the tags use is developed. In this case, the
neural network training is used to create a recommendation tag and marketable personalization tools. The customization algorithm
allows you to associate each user with a list of products that they are most likely to be interested in, and can predict what customers
might want to see even if they do not yet know about it. The developed method can be used to provide a more relevant set of content.
Also, the developed method gives the opportunity to classify the relevant content or show it earlier in the process of rolling the pages
to avoid consumers choosing the wrong content or spending time scrolling when looking for a product.
Results. The developed system is intended for distribution of information technology products (publications, books, courses,
videos, files, etc.) through the Internet.
Conclusions. Implementation of this system will allow access to certain types of content to the general public, since the site will
be placed on the World Wide Web; on the other hand, another part of the purpose of creating this system is a commercial component,
namely, the receipt of profits by the owner or administrator of the intellectual system, through the mechanisms of e- commerce.
Mobasher B. Data mining for web personalization, The adaptive web, 2007, Vol. 4321, pp. 90–135.
Dinucă C., Ciobanu D. Web Content Mining. In: University of Petroşani, Economics, 2012, Vol. 12, pp. 85–92.
Xu G., Zhang Y., Li L. Web content mining, Web Mining and Social Networking, 2011, Vol. 6, pp. 71–87.
Khribi M. K., Jemni M., Nasraoui O. Automatic recommendations for e-learning personalization based on
web usage mining techniques and information retrieval, Advanced Learning Technologies : International
Conference, 1–5 July 2008 : proceedings. Santander, Cantabria, Spain, IEEE, 2008, pp. 241–245.
Ferretti S., Mirri S., Prandi C., Salomoni P. Automatic web content personalization through reinforcement learning,
Journal of Systems and Software, 2016, Vol. 121, pp. 157–169.
Lavie T., Sela M., Oppenheim I., Inbar O., Meyer J. User attitudes towards news content personalization,
International journal of human-computer studies, 2010, Vol. 68(8), pp. 483–495.
Fredrikson M., Livshits B. Repriv: Re-imagining content personalization and in-browser privacy, Symposium on
Security and Privacy: Conference, 22–25 May 2011 : proceedings. Berkeley, CA, USA, IEEE, 20011, pp. 131–146.
Chang C., Chen P., Chiu F., Chen Y. Application of neural networks and Kano’s method to content recommendation in
web personalization, Expert Systems with Applications, 2009, Vol. 36(3), pp. 5310–5316.
Partovi H., Brathwaite R., Davis A., McCue M., Porter B., Giannandrea J., Li Z. (US) Pat. US7,571,226B1 US Content
personalization over an interface with adaptive voice character, U.S. ; TellMe Networks, Inc., Mountain View,
CA (US), No.: 09/523,853 ; Marz 14, 2009; August 4, 2009, Patent and Trademark Office, 20 p.
Kane F. J., Hicks C. (US) Pat. US2009/0171968A1 US Widget-assisted content personalization based on user
behaviors tracked across multiple web sites, Amazon Technologies Inc (US), No.: 11/966,817 ; December 28,
; Jule 2, 2009, Google Patents, 24 p.
Mirri S., Prandi C., Salomon P. Experiential adaptation to provide user-centered web content personalization,
Advances in Human oriented and Personalized Mechanisms, Technologies, and Services : The Sixth International
Conference, October 27 – November 1, 2013: proceedings. Venice, Italy, IARIA, 2003, pp. 31–36.
Fernandez-Luque L., Karlsen R., Bonander J. Review of extracting information from the Social Web for health
personalization, Journal of medical Internet research, 2011, Vol. 13(1), P. 15.
Hauser E. (US) Pat. US8,019,777B2 US Digital content personalization method and system /; CRICKET MEDIA
Inc (US). No.: 12/795,419 ; June 7, 2010; September 13, 2011, Patent and Trademark Office, 15 p.
Ho S. Y., Bodoff D., Tam K. Y. Timing of adaptive web personalization and its effects on online consumer behavior
Information Systems Research, 2011, Vol. 22(3), pp. 660–679.
Uchyigit G., Ma M. Y. Personalization techniques and recommender systems. Singapore, World Scientific, 2008,
Kothari N., Harder M., Howard R., Sanabria A., Schackow S. Pat. US2006/0020883A1 Web page
personalization / (US) ; Microsoft Technology Licensing LLC (US). No.: 10/857,724 ; May 28, 2004; Januar 26,
, Patent and Trademark Office. – 18 p.
Zhang H., Song Y., Song H. T. Construction of ontologybased user model for web personalization, Lecture Notes in
Computer Science, 2007, Vol. 4511, pp. 67–76.
Chien H. (US) Pat. US 8,254,892 B2 US Methods and apparatus for anonymous user identification and content
personalization in wireless communication; AT&T Mobility II LLC (US). No.: 12/468,708 ; September 10, 2009; August
, 2012, Patent and Trademark Office, 9 p.
Linden G. D., Smith B. R., Zada N. K. (US) Pat. US7,970,664B2 US Content personalization based on
actions performed during browsing sessions; Amazon Technologies Inc (US). No.: 11/009,732 ; December 10,
; June 28, 2011, Patent and Trademark Office. –36 p.
Mehtaa P., Parekh B., Modi K., Solanki P. Web personalization using web mining: concept and research
issue, International Journal of Information and Education Technology, 2012, Vol. 2(5), pp. 510–512.
Zhezhnych P., Markiv O. Linguistic Comparison Quality Evaluation of Web-Site Content with Tourism
Documentation Objects, Advances in Intelligent Systems and Computing, 2018, Vol. 689, pp. 656–667.
Basyuk T. The main reasons of attendance falling of internet resource, Computer Sciences and Information Technologies
: Xth International Scientific and Technical Conference, 14–17 September 2015 : proceedings. Lviv, IEEE, 2015,
Gozhyj A., Chyrun L., Kowalska-Styczen A., Lozynska O. Uniform Method of Operative Content Management in Web
Systems, CEUR Workshop Proceedings, 2018, Vol. 2136, pp. 62–77.
Kravets P. The control agent with fuzzy logic, Perspective Technologies and Methods in MEMS Design : VIth
International Conference, 20–23 April 2010 2015 : proceedings. Lviv, IEEE, 2015, pp. 40–41.
Davydov M., Lozynska O. Linguistic Models of Assistive Computer Technologies for Cognition and Communication,
Computer Science and Information Technologies : XIth International Scientific and Technical Conference, 6–10
September 2016 : proceedings. Lviv, IEEE, 2016, pp. 171–175.
Peleshko D., Ivanov Y., Sharov B., Izonin I., Borzov Y. Design and implementation of visitors queue density
analysis and registration method for retail videosurveillance purposes, Data Stream Mining & Processing : First
International Conference, 23–27 August 2016 : proceedings. Lviv, IEEE, 2016, pp. 159–162.
Ivanov Y., Peleshko D., Makoveychuk O., Izonin I., Malets I., Lotoshunska N., Batyuk D. Adaptive moving object
segmentation algorithms in cluttered environments, The Experience of Designing and Application of CAD Systems in
Microelectronics : Comference, 24 February 2015 : proceedings. Lviv, IEEE, 2015, pp. 97–99.
Vitynskyi P., Tkachenko R., Izonin I., Kutucu H. Hybridization of the SGTM Neural-like Structure through
Inputs Polynomial Extension, Data Stream Mining & Processing : Second International Conference, 21–25
August 2018 : proceedings. Lviv, IEEE, 2018, pp. 386–391.
Tkachenko R., Izonin I., Vitynskyi P., Lotoshynska N., and Pavlyuk O. Development of the Non-Iterative Supervised
Learning Predictor Based on the Ito Decomposition and SGTM Neural-Like Structure for Managing Medical
Insurance Costs, Data, 2018, Vol. 3(4), pp. 1–14.
Mykich K., Burov Y. Algebraic model for knowledge representation in situational awareness systems, Computer
Sciences and Information Technologies : 11th International Scientific and Technical Conference, 6–10 September 2016 :
proceedings. Lviv, IEEE, 2016, pp. 165–167.
Mykich K., Burov Y. Uncertainty in situational awareness systems, Modern Problems of Radio Engineering,
Telecommunications and Computer Science : 13th International Conference, 623–26 Februar 2016 :
proceedings. Lviv, IEEE, 2016, pp. 729–732.
Mykich K. Algebraic Framework for Knowledge Processing in Systems with Situational Awareness, Advances in
Intelligent Systems and Computing, 2017, Vol. 512, pp. 217–227.
Mykich K., Burov Y. Research of uncertainties in situational awareness systems and methods of their processing, Eastern
European Journal of Enterprise Technologies, 2016, Vol. 1(79), pp. 19–26.
Lytvyn V., Vysotska V., Veres O., Rishnyak I., Rishnyak H. The Risk Management Modelling in Multi Project
Environment, Computer Science and Information Technologies : 12th International Scientific and Technical
Conference, 5–8 September 2017 : proceedings. Lviv, IEEE, 2017, pp. 32–35.
Vysotska V. Linguistic Analysis of Textual Commercial Content for Information Resources Processing, Modern
Problems of Radio Engineering, Telecommunications and Computer Science : International Scientific and Technical
Conference, 23–26 February 2016 : proceedings. Lviv, IEEE, 2016, pp. 709–713.
Su J., Vysotska V., Sachenko A., Lytvyn V., Burov Y. Information resources processing using linguistic analysis of
textual content, Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications : 9th
International Conference, 21–23 September 2017 : proceedings. Bucharest, IEEE, 2017, pp. 573–578.
Lytvyn V., Vysotska V., Veres O., Rishnyak I., Rishnyak H. Content Linguistic Analysis Methods for Textual
Documents Classification, Computer Science and Information Technologies : 11th International Scientific and
Technical Conference, 6–10 September 2016 : proceedings. Lviv, IEEE, 2016, pp. 190–192.
Bisikalo O. V., Vysotska V. A. Identifying keywords on the basis of content monitoring method in ukrainian texts, Radio
Electronics, Computer Science, Control, 2016, Vol. 1(36), pp. 74–83.
Bisikalo O. V., Vysotska V. A. Sentence syntactic analysis application to keywords identification Ukrainian texts,
Radio Electronics, Computer Science, Control, 2016, Vol. 3(38), pp. 54–65.
Lytvyn V., Bobyk I., Vysotska V. Application of algorithmic algebra system for grammatical analysis of
symbolic computation expressions of propositional logic, Radio Electronics, Computer Science, Control, 2016,
Vol. 4(39), pp. 54–67.
Alieksieieva K., Berko A., Vysotska V. Technology of commercial web-resource management based on fuzzy
logic, Radio Electronics, Computer Science, Control, 2015, Vol. 3(34), pp. 71–79.
Korobchinsky M., Vysotska V., Chyrun L., Chyrun L. Peculiarities of Content Forming and Analysis in Internet
Newspaper Covering Music News, Computer Science and Information Technologies : 12th International Scientific and
Technical Conference, 5–8 September 2017 : proceedings. Lviv, IEEE, 2017, pp. 52–57.
Naum O., Chyrun L., Kanishcheva O., Vysotska V. Intellectual System Design for Content Formation,
Computer Science and Information Technologies : 12th International Scientific and Technical Conference, 5–8
September 2017 : proceedings. Lviv, IEEE, 2017, pp. 131–138.
Lytvyn Vasyl, Vysotska Victoria, Dosyn Dmytro, Holoschuk Roman, Rybchak Zoriana Application of
Sentence Parsing for Determining Keywords In Ukrainian Texts, Computer Science and Information Technologies :
th International Scientific and Technical Conference, 5–8 September 2017 : proceedings. Lviv, IEEE, 2017, pp. 326–331.
Vysotska V., Hasko R., Kuchkovskiy V. Process analysis in electronic content commerce system, Computer Science and
Information Technologies : Xth International Scientific and Technical Conference, 14–17 September 2015 :
proceedings. Lviv, IEEE, 2015, pp. 120–123.
Lytvyn V., Vysotska V. V. Designing architecture of electronic content commerce system, Computer Science and
Information Technologies : Xth International Scientific and Technical Conference, 14–17 September 2015 :
proceedings. Lviv, IEEE, 2015, pp. 115–119.
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