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Online Web Navigation Assistant. / Ali, No'aman Muhammad; Gadallah, Ahmed Mohamed; Hefny, Hesham Ahmed; Novikov, Boris Asenovich.

In: Vestnik Udmurtskogo Universiteta: Matematika, Mekhanika, Komp'yuternye Nauki, Vol. 31, No. 1, 01.03.2021, p. 116-131.

Research output: Contribution to journalArticlepeer-review

Harvard

Ali, NM, Gadallah, AM, Hefny, HA & Novikov, BA 2021, 'Online Web Navigation Assistant', Vestnik Udmurtskogo Universiteta: Matematika, Mekhanika, Komp'yuternye Nauki, vol. 31, no. 1, pp. 116-131. https://doi.org/10.35634/VM210109

APA

Ali, N. M., Gadallah, A. M., Hefny, H. A., & Novikov, B. A. (2021). Online Web Navigation Assistant. Vestnik Udmurtskogo Universiteta: Matematika, Mekhanika, Komp'yuternye Nauki, 31(1), 116-131. https://doi.org/10.35634/VM210109

Vancouver

Ali NM, Gadallah AM, Hefny HA, Novikov BA. Online Web Navigation Assistant. Vestnik Udmurtskogo Universiteta: Matematika, Mekhanika, Komp'yuternye Nauki. 2021 Mar 1;31(1):116-131. https://doi.org/10.35634/VM210109

Author

Ali, No'aman Muhammad ; Gadallah, Ahmed Mohamed ; Hefny, Hesham Ahmed ; Novikov, Boris Asenovich. / Online Web Navigation Assistant. In: Vestnik Udmurtskogo Universiteta: Matematika, Mekhanika, Komp'yuternye Nauki. 2021 ; Vol. 31, No. 1. pp. 116-131.

BibTeX

@article{f53a88d334f04336aab83dc4e44ca4ac,
title = "Online Web Navigation Assistant",
abstract = "The problem of finding relevant data while searching the internet represents a big challenge for web users due to the enormous amounts of available information on the web. These difficulties are related to the well-known problem of information overload. In this work, we propose an online web assistant called OWNA. We developed a fully integrated framework for making recommendations in real-time based on web usage mining techniques. Our work starts with preparing raw data, then extracting useful information that helps build a knowledge base as well as assigns a specific weight for certain factors. The experiments show the advantages of the proposed model against alternative approaches.",
keywords = "Link prediction, Recommender systems, Web log, Web mining, Web navigation assistant, Web personalization, Web usage mining",
author = "Ali, {No'aman Muhammad} and Gadallah, {Ahmed Mohamed} and Hefny, {Hesham Ahmed} and Novikov, {Boris Asenovich}",
note = "Publisher Copyright: {\textcopyright} 2021 Udmurt State University. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
month = mar,
day = "1",
doi = "10.35634/VM210109",
language = "English",
volume = "31",
pages = "116--131",
journal = "ВЕСТНИК УДМУРТСКОГО УНИВЕРСИТЕТА. МАТЕМАТИКА. МЕХАНИКА. КОМПЬЮТЕРНЫЕ НАУКИ",
issn = "1994-9197",
publisher = "Удмуртский государственный университет",
number = "1",

}

RIS

TY - JOUR

T1 - Online Web Navigation Assistant

AU - Ali, No'aman Muhammad

AU - Gadallah, Ahmed Mohamed

AU - Hefny, Hesham Ahmed

AU - Novikov, Boris Asenovich

N1 - Publisher Copyright: © 2021 Udmurt State University. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/3/1

Y1 - 2021/3/1

N2 - The problem of finding relevant data while searching the internet represents a big challenge for web users due to the enormous amounts of available information on the web. These difficulties are related to the well-known problem of information overload. In this work, we propose an online web assistant called OWNA. We developed a fully integrated framework for making recommendations in real-time based on web usage mining techniques. Our work starts with preparing raw data, then extracting useful information that helps build a knowledge base as well as assigns a specific weight for certain factors. The experiments show the advantages of the proposed model against alternative approaches.

AB - The problem of finding relevant data while searching the internet represents a big challenge for web users due to the enormous amounts of available information on the web. These difficulties are related to the well-known problem of information overload. In this work, we propose an online web assistant called OWNA. We developed a fully integrated framework for making recommendations in real-time based on web usage mining techniques. Our work starts with preparing raw data, then extracting useful information that helps build a knowledge base as well as assigns a specific weight for certain factors. The experiments show the advantages of the proposed model against alternative approaches.

KW - Link prediction

KW - Recommender systems

KW - Web log

KW - Web mining

KW - Web navigation assistant

KW - Web personalization

KW - Web usage mining

UR - http://www.scopus.com/inward/record.url?scp=85105478346&partnerID=8YFLogxK

U2 - 10.35634/VM210109

DO - 10.35634/VM210109

M3 - Article

AN - SCOPUS:85105478346

VL - 31

SP - 116

EP - 131

JO - ВЕСТНИК УДМУРТСКОГО УНИВЕРСИТЕТА. МАТЕМАТИКА. МЕХАНИКА. КОМПЬЮТЕРНЫЕ НАУКИ

JF - ВЕСТНИК УДМУРТСКОГО УНИВЕРСИТЕТА. МАТЕМАТИКА. МЕХАНИКА. КОМПЬЮТЕРНЫЕ НАУКИ

SN - 1994-9197

IS - 1

ER -

ID: 76923713