Artificial Intelligence and Decision Making
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Artificial Intelligence and Decision Making, 2021, Issue 4, Pages 3–17
DOI: https://doi.org/10.14357/20718594210401 (Mi iipr114) 		 
Decision analysis

Multi-aspect user ontology for decision support based on digital traces

A. V. Smirnov, T. V. Levashova

St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia
Full-text PDF (532 kB)
DOI: https://doi.org/10.14357/20718594210401
Abstract: The paper presents a multi-aspect user ontology that classifies a decision maker into a group of users with common preferences and decision-making behaviors and allows the decision support system to prognosticate a recommending a decision based on the information about the preferences and behaviors of this group. The ontology relies upon the conceptual framework of intelligent decision support to make recommendations based on user digital traces and comprises three independent aspects: user profile, user segment, and model of user digital life, integration of which is supported by an upper ontology level.
Keywords: decision support, multi-aspect user modelling, ontology model, ontology classification, user segmentation, digital traces.
Funding agency Grant number
Russian Foundation for Basic Research 20-07-00455
20-07-00490
Ministry of Science and Higher Education of the Russian Federation 0073-2019-0005
English version:
Scientific and Technical Information Processing, 2022, Volume 49, Issue 6, Pages 486–496
DOI: https://doi.org/10.3103/S0147688222060119
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: A. V. Smirnov, T. V. Levashova, “Multi-aspect user ontology for decision support based on digital traces”, Artificial Intelligence and Decision Making, 2021, no. 4, 3–17; Scientific and Technical Information Processing, 49:6 (2022), 486–496
Citation in format AMSBIB
\Bibitem{SmiLev21}
\by A.~V.~Smirnov, T.~V.~Levashova
\paper Multi-aspect user ontology for decision support based on digital traces
\jour Artificial Intelligence and Decision Making
\yr 2021
\issue 4
\pages 3--17
\mathnet{http://mi.mathnet.ru/iipr114}
\crossref{https://doi.org/10.14357/20718594210401}
\elib{https://elibrary.ru/item.asp?id=47367814}
\transl
\jour Scientific and Technical Information Processing
\yr 2022
\vol 49
\issue 6
\pages 486--496
\crossref{https://doi.org/10.3103/S0147688222060119}
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    		Artificial Intelligence and Decision Making
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    References:1
     
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