Learning different user profile annotated rules for fuzzy preference top-k querying

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F07%3A10109922" target="_blank" >RIV/00216208:11320/07:10109922 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Learning different user profile annotated rules for fuzzy preference top-k querying

Original language description

Uncertainty querying of large data can be solved by providing top-k answers according to a user fuzzy ranking/scoring function. Usually different users have different fuzzy scoring function - a user preference model. Main goal of this paper is to assigna user a preference model automatically. To achieve this we decompose user's fuzzy ranking function to ordering of particular attributes and to a combination function. To solve the problem of automatic assignment of user model we design two algorithms, one for learning user preference on particular attribute and second for learning the combination function. Methods were integrated into a Fagin-like top-k querying system with some new heuristics and tested

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

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Project

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Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2007

Confidentiality

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

Name of the periodical

Lecture Notes in Computer Science

ISSN

0302-9743

e-ISSN

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Volume of the periodical

2007

Issue of the periodical within the volume

4772

Country of publishing house

DE - GERMANY

Number of pages

15

Pages from-to

116-130

UT code for WoS article

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EID of the result in the Scopus database

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