Beyond Model Complexity: A Reproducible Comparison of Classical Machine Learning, Matrix Factorization, Graph Embeddings, and LightGCN for Recommendation
- DOI
- 10.3390/a19090735
- Published
- 2026-09-01
- Container
- Algorithms
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/a19090735,
title = {Beyond Model Complexity: A Reproducible Comparison of Classical Machine Learning, Matrix Factorization, Graph Embeddings, and LightGCN for Recommendation},
author = {Rodolfo Bojorque and David Yánez-Peter and Miguel Arcos-Argudo},
year = {2026},
journal = {Algorithms},
doi = {10.3390/a19090735},
url = {https://doi.org/10.3390/a19090735}
}RIS
TY - JOUR TI - Beyond Model Complexity: A Reproducible Comparison of Classical Machine Learning, Matrix Factorization, Graph Embeddings, and LightGCN for Recommendation AU - Rodolfo Bojorque AU - David Yánez-Peter AU - Miguel Arcos-Argudo PY - 2026 JO - Algorithms DO - 10.3390/a19090735 UR - https://doi.org/10.3390/a19090735 ER -
APA
Bojorque, R., Yánez-Peter, D., & Arcos-Argudo, M. (2026). Beyond Model Complexity: A Reproducible Comparison of Classical Machine Learning, Matrix Factorization, Graph Embeddings, and LightGCN for Recommendation. Algorithms. https://doi.org/10.3390/a19090735
Source records
- crossref · retrieved 2026-09-24T22:41:50.302Z