Journal
APPLIED SCIENCES-BASEL
Volume 10, Issue 2, Pages -Publisher
MDPI
DOI: 10.3390/app10020675
Keywords
genetic programming; recommender systems; collaborative filtering; matrix factorization
Categories
Funding
- Instituto de Ciencias Matematicas through a Severo Ochoa Postdoctoral Fellowship [SOLAUT_00030167]
- Spanish Ministry of Science and Education and Competitivity (MINECO)
- European Regional Development Fund (FEDER) [TIN2017-85727-C4-3-P]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available