An extended proximity relation and quantified aggregation for designing robust fuzzy query engine

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

An extended proximity relation and quantified aggregation for designing robust fuzzy query engine

Popis výsledku v původním jazyce

In this article, we propose a novel model of a robust fuzzy query engine that addresses vagueness in data and users&apos; 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.

Název v anglickém jazyce

An extended proximity relation and quantified aggregation for designing robust fuzzy query engine

Popis výsledku anglicky

In this article, we propose a novel model of a robust fuzzy query engine that addresses vagueness in data and users&apos; 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Knowledge-Based Systems

ISSN

0950-7051

e-ISSN

1872-7409

Svazek periodika

290

Číslo periodika v rámci svazku

April 2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

111574

Kód UT WoS článku

001221709900001

EID výsledku v databázi Scopus

2-s2.0-85186384332