David Yánez-Peter

Beyond Model Complexity: A Reproducible Comparison of Classical Machine Learning, Matrix Factorization, Graph Embeddings, and LightGCN for Recommendation

Findings show that under the evaluated setting, greater model complexity did not consistently translate into higher recommendation effectiveness, and they thus highlight the importance of strong baselines, model tuning, standardized evaluation, and reproducible experimental protocols.