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Automatic grammatical evolution-based optimization of matrix factorization algorithm
ID
Kunaver, Matevž
(
Author
),
ID
Bűrmen, Árpád
(
Author
),
ID
Fajfar, Iztok
(
Author
)
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MD5: 427553D3C5E505524CBDE0BA4696DE10
URL - Source URL, Visit
https://www.mdpi.com/2227-7390/10/7/1139
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Abstract
Nowadays, recommender systems are vital in lessening the information overload by filtering out unnecessary information, thus increasing comfort and quality of life. Matrix factorization (MF) is a well-known recommender system algorithm that offers good results but requires a certain level of system knowledge and some effort on part of the user before use. In this article, we proposed an improvement using grammatical evolution (GE) to automatically initialize and optimize the algorithm and some of its settings. This enables the algorithm to produce optimal results without requiring any prior or in-depth knowledge, thus making it possible for an average user to use the system without going through a lengthy initialization phase. We tested the approach on several well-known datasets. We found our results to be comparable to those of others while requiring a lot less set-up. Finally, we also found out that our approach can detect the occurrence of over-saturation in large datasets.
Language:
English
Keywords:
matrix factorization
,
genetic programming
,
grammatical evolution
,
recommender systems
,
meta-optimization
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
FE - Faculty of Electrical Engineering
Publication status:
Published
Publication version:
Version of Record
Year:
2022
Number of pages:
22 str.
Numbering:
Vol. 10, iss. 7, art. 1139
PID:
20.500.12556/RUL-136721
UDC:
004
ISSN on article:
2227-7390
DOI:
10.3390/math10071139
COBISS.SI-ID:
103328771
Publication date in RUL:
18.05.2022
Views:
612
Downloads:
118
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Record is a part of a journal
Title:
Mathematics
Shortened title:
Mathematics
Publisher:
MDPI AG
ISSN:
2227-7390
COBISS.SI-ID:
523267865
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:
01.04.2022
Secondary language
Language:
Slovenian
Keywords:
matrična faktorizacija
,
genetsko programiranje
,
slovnična evolucija
,
priporočilni sistemi
,
metaoptimizacija
Projects
Funder:
ARRS - Slovenian Research Agency
Project number:
P2-0246
Name:
ICT4QoL - Informacijsko komunikacijske tehnologije za kakovostno življenje
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