Automatic grammatical evolution-based optimization of matrix factorization algorithm

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

Povzetek

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.

Ključne besede

matrična faktorizacija;genetsko programiranje;slovnična evolucija;priporočilni sistemi;meta-optimizacija;matrix factorization;genetic programming;grammatical evolution;recommender systems;meta-optimization;

Podatki

Jezik:	Angleški jezik
Leto izida:	2022
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL FE - Fakulteta za elektrotehniko
UDK:	004
COBISS:	103328771
ISSN:	2227-7390
Št. ogledov:	88
Št. prenosov:	52
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarni naslov:	Elektronski vir
Sekundarne ključne besede:	matrična faktorizacija;genetsko programiranje;slovnična evolucija;priporočilni sistemi;metaoptimizacija;
Vrsta dela (COBISS):	Članek v reviji
Strani:	str. 1-22
Letnik:	ǂiss. ǂ7
Zvezek:	1139
Čas izdaje:	Apr.-1 2022
DOI:	10.3390/math10071139
ID:	15347661