Automatic grammatical evolution-based optimization of matrix factorization algorithm

Kunaver, Matevž (author), Bürmen, Arpad (author), Fajfar, Iztok (author)

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

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