PyTorch Implementation of Matrix Factorization

Matrix Factorization (MF) (e.g., Probabilistic Matrix Factorization and NonNegative Matrix Factorization) techniques have become the crux of many real-world scenarios, including graph representation and recommendation system (RecSys) because they are powerful models to find the hidden properties behind the data. The idea behind matrix factorization is to represent users and items in a lower-dimensional latent space. And is widely used in the recommendation system and dimensionality reduction. Although, there are many Python libraries that could perform matrix factorization, building the algorithm from scratch could be helpful to understand the basics. Also, there are many complex cases when the matrix factorization library could not handle.