The library used for recommendation is https://github.com/rexyai/rsparse fallowing this excellent post http://dsnotes.com/post/2017-05-28-matrix-factorization-for ...

Matrix factorization is a powerful technique for building recommender systems that can predict user ... using a loss function and an optimization algorithm, such as gradient descent.

Matrix factorization assumes that each user and item can be represented by a vector of latent features, such as genre, style, quality, or popularity. These features capture the underlying patterns ...

Forbes, P. & Zhu, M., 2011. Content-boosted matrix factorization for recommender systems: Experiments with recipe recommendation. In Proceedings of the 5th ACM ...

Abstract: This paper presents an improvement of bounded-SVD bias -- a matrix factorization (MF) method for recommender systems. In bounded-SVD bias, the bound constraints are included in the objective ...

The system could be part of a retail website, an online bookstore, a movie rental service or an online education portal and so on. In this paper, I will focus on matrix factorization algorithms as ...

Abstract: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the ...