Evolving Matrix-Factorization-Based Collaborative Filtering Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @Article{lara-cabrera:2020:AS,
-
author = "Raul Lara-Cabrera and Angel Gonzalez-Prieto and
Fernando Ortega and Jesus Bobadilla",
-
title = "Evolving {Matrix-Factorization-Based} Collaborative
Filtering Using Genetic Programming",
-
journal = "Applied Sciences",
-
year = "2020",
-
volume = "10",
-
number = "2",
-
keywords = "genetic algorithms, genetic programming",
-
ISSN = "2076-3417",
-
URL = "https://www.mdpi.com/2076-3417/10/2/675",
-
DOI = "doi:10.3390/app10020675",
-
abstract = "Recommender systems aim to estimate the judgment or
opinion that a user might offer to an item.
Matrix-factorization-based collaborative filtering
typifies both users and items as vectors of factors
inferred from item rating patterns. This method finds
latent structure in the data, assuming that
observations lie close to a low-dimensional latent
space. However, matrix factorizations have been
traditionally designed by hand. Here, we present
Evolutionary Matrix Factorization (EMF), an
evolutionary approach that automatically generates
matrix factorizations aimed at improving the
performance of recommender systems. Initial experiments
using this approach show that EMF generally outperforms
baseline methods when applied to MovieLens and
FilmTrust datasets, having a similar performance to
those baselines on the worst cases. These results serve
as an incentive to continue improving and studying the
application of an evolutionary approach to
collaborative filtering based on Matrix
Factorization.",
-
notes = "also known as \cite{app10020675}",
- }
Genetic Programming entries for
Raul Lara-Cabrera
Angel Gonzalez-Prieto
Fernando Ortega
Jesus Bobadilla
Citations