Evolving Matrix-Factorization-Based Collaborative Filtering Using Genetic Programming
by
Raúl Lara-Cabrera 1,*,†
, Ángel González-Prieto 1,2,†, Fernando Ortega 1,† and Jesús Bobadilla 1,†
Raúl Lara-Cabrera 1,*,†, Ángel González-Prieto 1,2,†, Fernando Ortega 1,† and Jesús Bobadilla 1,†
1
Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, 28031 Madrid, Spain
2
Instituto de Ciencias Matemáticas, CSIC-UAM-UC3M-UCM, 28049 Madrid, Spain
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2020, 10(2), 675; https://doi.org/10.3390/app10020675
Received: 24 December 2019 / Revised: 10 January 2020 / Accepted: 13 January 2020 / Published: 18 January 2020
(This article belongs to the Section Computing and Artificial Intelligence)
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.
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MDPI and ACS Style
Lara-Cabrera, R.; González-Prieto, Á.; Ortega, F.; Bobadilla, J. Evolving Matrix-Factorization-Based Collaborative Filtering Using Genetic Programming. Appl. Sci. 2020, 10, 675.
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