Abstract: Matrix factorization is one of the best approaches for collaborative filtering because of its high accuracy in presenting users and items latent factors. The main disadvantages of matrix factorization are its complexity, and are very hard to be parallelized, especially with very large matrices. In this paper, we introduce a new method for collaborative filtering based on Matrix Factorization by combining simulated annealing with levy distribution. By using this method, good solutions are achieved in acceptable time with low computations, compared to other methods like stochastic gradient descent, alternating least squares, and weighted non-negative matrix factorization.

Mostafa A. Shehata, Mohammad Nassef and Amr A. Badr, “Simulated Annealing with Levy Distribution for Fast Matrix Factorization-Based Collaborative Filtering” International Journal of Advanced Computer Science and Applications(IJACSA), 9(4), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090445

@article{Shehata2018,
title = {Simulated Annealing with Levy Distribution for Fast Matrix Factorization-Based Collaborative Filtering},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090445},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090445},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {4},
author = {Mostafa A. Shehata and Mohammad Nassef and Amr A. Badr}
}