Simulated Annealing with Levy Distribution for Fast Matrix Factorization-Based Collaborative Filtering

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 t...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced computer science & applications 2018, Vol.9 (4)
Main Authors: Shehata, Mostafa A., Nassef, Mohammad, A., Amr
Format: Article
Language:English
Subjects:
Collaboration
Factorization
Filtration
Levy distribution
Simulated annealing
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2018.090445