An extended proximity relation and quantified aggregation for designing robust fuzzy query engine
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F24%3A10254931" target="_blank" >RIV/61989100:27510/24:10254931 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0950705124002090" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0950705124002090</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.knosys.2024.111574" target="_blank" >10.1016/j.knosys.2024.111574</a>
Alternative languages
Result language
angličtina
Original language name
An extended proximity relation and quantified aggregation for designing robust fuzzy query engine
Original language description
In this article, we propose a novel model of a robust fuzzy query engine that addresses vagueness in data and users' requirements. It aims to assist users in recommending similar products or services by retrieving the most suitable entities when the limitations of queries and recommendation approaches are recognized. The proposed fuzzy engine model considers various complex aspects, including imprecise preferences explained by linguistic terms, uncertain data in datasets, connections among elementary requirements, and the lack of historical data necessary for personalized recommendations. To achieve this goal, we propose a similarity matching based on the extended proximity relation and an adapted conformance measure for elementary requirements. In this direction, a new monotonicity property for proximity relation is introduced to ensure consistency in similarities among ordinal categorical data (including binary data) expressed by crisp or fuzzy numbers. Therefore, the conformance measure used to evaluate the similarity between user requirements and attribute values is expressed as a fuzzy number. Next, we propose a quantified aggregation of elementary requirements by strictly monotone fuzzy relative quantifiers. The flexibility is further extended by a convex combination of possibility and necessity measures. A hotel selection experiment is being carried out to explore the potential of the proposed fuzzy query engine. Finally, the limitations and usefulness of the proposed approach are addressed.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Knowledge-Based Systems
ISSN
0950-7051
e-ISSN
1872-7409
Volume of the periodical
290
Issue of the periodical within the volume
April 2024
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
111574
UT code for WoS article
001221709900001
EID of the result in the Scopus database
2-s2.0-85186384332