Automatic Grammatical Evolution-Based Optimization of Matrix Factorization Algorithm

Nowadays, recommender systems are vital in lessening the information overload by filtering out unnecessary information, thus increasing comfort and quality of life. Matrix factorization (MF) is a well-known system algorithm that offers good results but requires certain level knowledge some effort on part user before use. In this article, we proposed an improvement using grammatical evolution (GE) to automatically initialize optimize its settings. This enables produce optimal without requiring any prior or in-depth knowledge, making it possible for average use going through lengthy initialization phase. We tested approach several datasets. found our be comparable those others while lot less set-up. Finally, also can detect occurrence over-saturation large